What are the total number of action potentials in the human brain?

Is there an approximate figure of the total number of action potentials in the human brain? It's my understanding that there are ~ 60 billion neurons in the brain with ~ 100 trillion connections between them. Plus there are graded responses between those connections. Is there an estimated figure for what the total might be?

If firing rate is from 1 Hz to 200 Hz, 100 trillion to 20 quadrillion synaptic firings. Neuronal (say, measured from soma) firings will add up to 86 billion to 17.2 trillion action potentials per second.

It important to remember, that synaptic firings "sum up" in soma or interfere between each other, so the are more of those. Read more:

Number of neurons in the brain

The number of neurons in the brain is about 10¹¹. For instance, Azevado et al physically counted them and found 0.6-1 * 10¹¹. Eric Chudler has collected estimates from a range of textbooks, which estimate 1-2 x 10¹⁰ of these (10%-30%) are in the cerebral cortex. 1

Number of synapses in the brain

The number of synapses in the brain is known much less precisely, but is probably about 10¹⁴. For instance reports 10¹⁴-10¹⁵ (100 – 1000 trillion) synapses in the brain, with no citation or explanation. Wikipedia says the brain contains 100 billion neurons, with 7,000 synaptic connections each, for 7 x 10¹⁴ synapses in total, but this seems possibly in error. 2

Number of synapses in the neocortex

One way to estimate of the number of synapses in the brain is to extrapolate from the number in the neocortex. According to stereologic studies that we have not investigated, there are around 1.4 x 10¹⁴ synapses in the neocortex. 3 This is roughly consistent with Eric Chudler’s summary of textbooks, which gives estimates between 0.6-2.4 x 10¹⁴ for the number of synapses in the cerebral cortex. 4

We are not aware of convincing estimates for synaptic density outside of the cerebral cortex, and our impression is that widely reported estimates of 10¹⁴ are derived from the assumption that the neocortex contains the great bulk of synapses in the brain. This seems plausible given the large volume of the neocortex, despite the fact that it contains a minority of the brain’s neurons. By volume, around 80% of the human brain is neocortex. 5 The neocortex also consumes around 44% of the brain’s total energy, which may be another reasonable indicator of the fraction of synapses in contains. 6 So our guess is that the number of synapses in the entire brain is somewhere between 1.3 and 2.3 times the number in the cerebral cortex. From above, the cerebral cortex contains around 1.4 x 10¹⁴ synapses, so this gives us 1.8-3.2 x 10¹⁴ total synapses.

Number of synapses per neuron

The number of synapses per neuron varies considerably. According to Wikipedia, the majority of neurons are cerebellum granule cells, which have only a handful of synapses, while the statistics above suggest that the average neuron has around 1,000 synapses. Purkinje cells have up to 200,000 synapses. 7

Number of glial cells in the brain

Azevado et al aforementioned investigation finds about 10¹¹ glial cells (the same as the number of neurons).

Relevance of cells other than neurons to computations in the brain

It seems that the timescales of glial dynamics are substantially longer than for neuron dynamics. Sandberg and Bostrom write: “However, the time constants for glial calcium dynamics is generally far slower than the dynamics of action potentials (on the order of seconds or more), suggesting that the time resolution would not have to be as fine” (p. 36). This suggests that the computational role of glial cells is not too great. References to much larger numbers of glial cells appear to be common, but we were unable to track down any empirical research supporting these claims. An informal blog post suggests that a common claim that there are ten times as many glial cells as neurons may be a popular myth.

We are not aware of serious suggestions that cells other than neurons or glia play a computationally significant role in the functioning of the brain.

  1. Total number of neurons in cerebral cortex = 10 billion (from G.M. Shepherd, The Synaptic Organization of the Brain, 1998, p. 6). However, C. Koch lists the total number of neurons in the cerebral cortex at 20 billion (Biophysics of Computation. Information Processing in Single Neurons, New York: Oxford Univ. Press, 1999, page 87).
  2. “The human brain has a huge number of synapses. Each of the 10 11 (one hundred billion) neurons has on average 7,000 synaptic connections to other neurons. It has been estimated that the brain of a three-year-old child has about 10¹⁵ synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10¹⁴ to 5 x 10¹⁴ synapses (100 to 500 trillion).” Wikipedia accessed April 13 󈧓, citing “Do we have brain to spare?“. Neurology64 (12): 2004–5. We have not accessed most of the Drachman paper, but it does at least say “Within the liter and a half of human brain, stereologic studies estimate that there are approximately 20 billion neocortical neurons, with an average of 7,000 synaptic connections each”. This suggests that the Wikipedia page errs in attributing the 7,000 synaptic connections per neuron to the brain at large instead of the neocortex.
  3. “Within the liter and a half of human brain, stereologic studies estimate that there are approximately 20 billion neocortical neurons, with an average of 7,000 synaptic connections each”.”Do we have brain to spare?“. Neurology64 (12): 2004–5.
  4. “Number of synapses in cortex = 0.15 quadrillion (Pakkenberg et al., 1997 2003)… [the ‘cortex’ probably refers either the cerebral cortex or the neocortex, which is part of and thus should be smaller than the cerebral cortex.]

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What does a neuron look like?

A useful analogy is to think of a neuron as a tree. A neuron has three main parts: dendrites, an axon, and a cell body or soma (see image below), which can be represented as the branches, roots and trunk of a tree, respectively. A dendrite (tree branch) is where a neuron receives input from other cells. Dendrites branch as they move towards their tips, just like tree branches do, and they even have leaf-like structures on them called spines.

The axon (tree roots) is the output structure of the neuron when a neuron wants to talk to another neuron, it sends an electrical message called an action potential throughout the entire axon. The soma (tree trunk) is where the nucleus lies, where the neuron’s DNA is housed, and where proteins are made to be transported throughout the axon and dendrites.

The tree-like structure of a neuron. Dendritic spines are small structures that receive inputs from the axons of other neurons. Bottom-right image: a segment of dendrite from which spines branch off, like leaves off a tree branch. Note the very small size (

0.001mm). (Image: Alan Woodruff De Roo et al / CC BY-SA 3.0 via Commons)

There are different types of neurons, both in the brain and the spinal cord. They are generally divided according to where they orginate, where they project to and which neurotransmitters they use.

Human Nervous System: Function and Types (with diagram)

1. Sensory input, that is, the detection of stimuli by the receptors, or sense organs (e.g., eyes, ears, skin, nose and tongue).

2. Transmission of this input by nerve impulses to the brain and spinal cord, which generate an appropriate response.

3. Motor output, that is, carrying out of the response by muscles or glands, which are called effectors.

Two types of cells constitute the nervous system— neurons and neuroglia. The neurons conduct impulses and the neuroglia support and protect the neurons. A neuron consists of a cell body called cyton, and two types of processes—dendrite and axon.

Dendrites or Dendron’s:

These are hair like processes connected to the cyton. They receive stimulus, which may be physical, chemical, mechanical or electrical, and pass it on to the cyton.

It is the cell body, with a central nucleus surrounded by cytoplasm.

From one side of the cyton arises a cylindrical process filled with cytoplasm. This process is called axon. It is the longest part of the neuron. It transmits impulse away from the cyton. Its tip has a swelling called axon bulb. Generally, a neuron has one axon.

The ending of an axon may be branched. These endings are called synaptic terminals. The gap between a synaptic terminal and the dendrite of another neuron or an effecter cell is called a synapse.

How do we feel a hot or cold object? How do we feel pain? Why do different things have different smells and tastes? There are thousands of receptor cells in our sense organs. They detect stimuli such as heat, cold, pain, smells and tastes.

There are different types of receptors such as algesireceptors (for pain), tango receptors (for touch), gustatoreceptors (for taste), olfactoreceptors (for smell), and so on. The stimulus received by a receptor is passed on in the form of electrical signals through the dendrites of a neuron to the cyton of the neuron.

The cyton transmits only strong impulses. Weak impulses are not further transmitted. An impulse passed on by the cyton travels along the axon of the neuron. When it reaches the end of the axon, it causes the axon bulb to release a chemical which diffuses across the synapse and stimulates the dendrites of the adjacent neuron.

These dendrites in turn send electrical signals to their cell body, to be carried along the axon. In this way, the sensation from the receptor is passed on to the brain or spinal cord. A signal from the brain is similarly passed on to the effector, which carries out the appropriate response.

Eat some sugar. You will find it tastes sweet. If you block your nose with your fingers there is no difference in its taste. It still tastes sweet because sugar has no smell that can also contribute to the taste.

Block your nose again while eating lunch. You will find that the blocked nose makes a difference in appreciating the taste of various food items. When an item has taste as well as smell, it needs the gustatoreceptors on the tongue as well as the olfactoreceptors in the nose to transmit its stimuli to the brain for the full appreciation of its taste.

For example, you may not be able to distinguish between mashed papaya and mashed banana with your nose blocked and eyes closed. The gustatoreceptors and olfactoreceptors together make us appreciate any food better. This is the reason why food seems tasteless when you have a cold and your nose is blocked.

In humans and vertebrates, the nervous system may be divided into the (1) central, (2) peripheral, and (3) autonomic nervous system.

Central nervous system:

The central nervous system consists of the brain and the spinal cord.


It is the most important coordinating centre in the body. It is lodged in the brain box, or cranium, which protects it. The brain is covered by membranes called meninges. Between the membranes and the brain and also inside the brain, there is a characteristic fluid, called cerebrospinal fluid. This also protects the brain.

The brain may be divided into three parts—forebrain, midbrain and hindbrain:

1. The forebrain (cerebrum) is the anterior part, consisting of two large hemispheres divided by a longitudinal fissure. The surface of the hemispheres has many folds and is called cerebral cortex. The cerebral cortex consists of numerous neurons, and the folds serve to increase the surface area so that the maximum number of neurons can be present.

The cerebral hemispheres are seats of intelligence and voluntary action. The forebrain also contains olfactory lobes, which are the centres of smell and the diencephalon, which has centres of hunger, thirst, etc. To the floor of the diencephalon is attached the pituitary gland.

2. The midbrain includes optic lobes, which are the centres of vision.

3. The hindbrain is the posterior part, located below the forebrain. It consists of the cerebellum, pons and medulla oblongata. The cerebellum is the coordination centre, and maintains the body’s posture and balance. It also controls some precise voluntary actions such as those involved in writing and speech.

The medulla oblongata in the brain stem is the centre of involuntary actions, like swallowing, coughing, sneezing, salivation, vomiting, heartbeat and breathing. The medulla oblongata is continued into the spinal cord. The pons relays information between the cerebellum and the cerebrum.

Spinal cord:

It is a long cord which arises from the medulla oblongata and rims through the vertebral column (backbone). The vertebral column protects the spinal cord. The spinal cord is also covered by meninges.

A cross sections of the spinal cord shows the central canal, which is filled with cerebrospinal fluid. Around the canal are clusters of cytons, which form the grey matter.

The peripheral part has mainly axons and is called white matter. From each side of the spinal cord two roots, the dorsal and the ventral root, arise.

The dorsal root is joined by a nerve called sensory nerve, which picks up sensations from the sense organs (receptors). From the ventral root arises the motor nerve, which takes messages from the spinal cord to the muscles or glands (effectors).

Reflex action:

What happens when you touch something hot or your finger is pricked by a needle? You immediately pull your hand away, without even thinking why you are doing so. Such sudden involuntary responses to stimuli are examples of reflex action. The response may be different when your conscious thought process is involved. For example, when a doctor pricks you with an injection needle to inject a medicine into your arm, you do not withdraw your arm immediately.

Your conscious thinking tells you that the medicine is being administered to cure your disease. In this case, a message from the spinal cord goes to the cerebrum, the thinking part of your brain, and your thinking brain directs your arm to bear the pain and not pull away.

The spinal cord is the centre of reflex action. Reflex actions are produced by reflex arcs, which may be formed anywhere along the spinal cord, nearest to the receptor and effecter. A reflex arc is formed by a sensory nerve and a motor nerve joined by a connecting nerve present in the spinal cord.

As the impulses do not have to travel all the way to the brain and back, the detection of stimuli and the completion of responses are faster.

Reflex action is an extremely quick action, which does not involve any thinking by the brain. If someone hits your leg with a hammer the leg is immediately withdrawn. In this type of reflex action the impact of the hammer (stimulus) received by the receptor is sent to the spinal cord through the sensory nerve. The message is received by the connecting nerve in the spinal cord.

The connecting nerve then sends a response through the motor nerve to the muscles (effectors) to pull the leg away. Thus, reflex action is a sudden, involuntary motor response to a stimulus. The flow of food in the alimentary canal, blinking in strong light or in response to a sudden movement in front of the eye, sneezing, coughing, yawning, hiccupping, shivering, etc., are also reflex actions.

Peripheral nervous system:

The peripheral nervous system includes 12 pairs of cranial nerves arising from the brain and 31 pairs of spinal nerves arising from the spinal cord. The nerves from the brain and the spinal cord connect the skeletal muscles and control their activity according to the directions and demands of the body. These nerves are, therefore, related to voluntary acts, i.e., they act according to our will.

Autonomic nervous system:

The autonomic nervous system controls and integrates the functions of internal organs like the heart, blood vessels, glands, etc., which are not under the control of our will.

The autonomic nervous system has two subdivisions: sympathetic and parasympathetic. The organs receive both sympathetic and parasympathetic nerves. The two types of nerves have opposite effects on the organs, i.e., if one is stimulatory, the other is inhibitory.

How does the nervous tissue cause the muscles to act?

When an electrical signal from a nerve cell reaches a synapse it causes the axon bulb to release a chemical. This chemical, which is discharged at the junction between the nerve cell and the muscle cell, causes the cell membrane of the muscle cell to move some ions in the muscle cell. This triggers a series of changes, ultimately causing the muscle to contract or relax.


  • Each receptor contains 2 subunits designated T1R2 and T1R3 and is
  • coupled to G proteins.
  • The complex of G proteins has been named gustducin because of its similarity in structure and action to the transducin that plays such an essential role in rod vision.
  • Activation of gustducin triggers a cascade of intracellular reactions:
    • production of the second messengers inositol trisphosphate (IP3) and diacylglycerol (DAG) which
    • releases intracellular stores of Ca ++ which
    • allows in the influx of Na + ions depolarizing the cell and causing the
    • release of ATP, which
    • triggers action potentials in a nearby sensory neuron.

    The hormone leptin inhibits sweet cells by opening their K + channels. This hyperpolarizes the cell making the generation of action potentials more difficult. Could leptin, which is secreted by fat cells, be a signal to cut down on sweets?


    Fatt P, Katz B (1951): An analysis of the end-plate potential recorded with an intracellular electrode. J. Physiol. (Lond.) 115: 320-70.

    Granit R, Haase J, Rutledge LT (1960): Recurrent inhibition in relation to frequency of firing and limitation of discharge rate of extensor motoneurons. J. Physiol. (Lond.) 154: 308-28.

    Granit R, Renkin B (1961): Net depolarization and discharge rate of motoneurons, as measured by recurrent inhibition. J. Physiol. (Lond.) 158: 461-75.

    Hille B (1970): Ionic channels in nerve membranes. Prog. Biophys. Mol. Biol. 21: 1-32.

    Loewenstein WR (1959): The generation of electric activity in a nerve ending. Ann. N.Y. Acad. Sci. 81: 367-87.

    Schmidt RF (ed.) (1981): Fundamentals of Sensory Physiology, 2nd ed., 286 pp. Springer-Verlag, New York, Heidelberg, Berlin.

    Stevens CF (1968): Synaptic physiology. Proc. IEEE 56:(6) 916-30. (Special issue on studies of neural elements and systems).

    Takeuchi A, Takeuchi N (1960): On the permeability of end-plate membrane during the action of transmitter. J. Physiol. (Lond.) 154: 52-67.

    The Cerebral Cortex

    The cerebral cortex is the part of the brain that makes human beings unique. Functions that originate in the cerebral cortex include:

    The cerebral cortex is what we see when we look at the brain. It is the outermost portion that can be divided into four lobes.

    Each bump on the surface of the brain is known as a gyrus, while each groove is known as a sulcus.

    What are the total number of action potentials in the human brain? - Biology

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    Events in Visual Perception

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    Which of the following best explains the voltmeter reading?

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    Events in Visual Perception

    1.Potassium channels opens, allowing potassium ions to move out. 2.Some sodium channels open in the dendrite.3. All ion channels close and the Na+/K+ exchange pump starts working. 4.Sodium ions diffuse toward the first node of Ranvier in the axon 5.Lights hit the retina. 6The threshold level is reached at the first node of Ranvier in the axon 7. Sodium channels open in the neural membrane of the axon, allowing the remaining sodium ions to move in.

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    Which of the following choices correctly fills in the blanks?

    After repolarization, the sodium ions are found _____A_______ the neuron and the potassium ions are found ______B_____ of the neuron. This reversal of ions can be fixed by the action of ____________C______________. ____D____ sodium ions are taken out and ___E____ potassium ions are brought in.

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    The nerve impulses always travel in one direction from dendrites to axon terminals. What prevents an action potential from travelling backward?

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    The interaction of a photon with the photoreceptors in the retina first causes the movement of sodium ions into a sensory neuron. What happens after the movement of sodium ions?

    A neuron was isolated from structure number 3 in the diagram above.

    When comparing the velocity of action potentials, structure numbered 3 is much slower than that of structure numbered 4.

    Which of the following explains why this is?

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    Key Terms: - Dendrite - Soma - Axon - Afferent Neuron - Efferent Neuron - Interneuron - RMP (resting membrane potential) - Depolarization - Repolarization - Hyperpolarization - Refractory Period - All-or-nothing law - Glia (neural cells that form the myelin sheath) - Nodes of Ranvier (little gaps in the myelin sheath) - Graded Potential (little impulses that change the membrane potential. Vary in strength and size due to the strength of the stimulus that causes them) - Action Potential (lots of little graded potentials added together to cause an action potential)

    Brain Lobes: - Frontal Lobe - The executive lobe of the brain - Decision making and planning - Temporal Lobe - Memory - Damage of the temporal lobe leads to amnesia - Parietal Lobe - Movement - Occipital Lobe - Vision

    Neurons - Neurons are the brains processing units (specialized nerve cells) - In 0.5 mm of brain tissue, you can see two neurons - There are approximately 85 billion in the average human brain. Each makes between 100-10,000 connections with others - The main components of the neuron are ​Dendrites, Cell body/soma, and the Axon - Neural Transmission occurs through the myelin sheath. The myelin sheath is an insulating layer around nerves allowing the neurons to travel faster along the axon. (neural communication)

    Information flow goes from cell body to presynaptic terminal

    The three different types of neurons are ● Afferent (info into the CNS) 'ascending' ● Efferent (info out of the CNS) 'descending' ● Interneuron (connections within the CNS)

    Resting Membrane Potential and Action Potentials: - Resting membrane potential = rmp - The inside of a neuron is negatively charged compared to the outside - Neurons have a resting membrane potential of about -70mv - RMP occurs because selective membrane ion channels control the movement of ions in and out of the cell - The sodium-potassium pump is key for maintaining RMP and in creating an action potential - The pump moves Na+ ions and K+ ions from outside to inside the cell (and vice versa)

    The main steps of an Action Potential - Threshold - This is approximately -50mv and is what the stimulus needs to reach in order for the reaction potential to be generated - Depolarization - During an action potential ions cross in and out of the cells membrane - In depolarization, Na+ ions rush into the cell from outside. This causes the cell to become more positive - The inside of the cell becomes more positive than the outside and a threshold is reached

    Chapter 2: Neurotransmitters, Synapses and Dopamine

    Key Terms: - Synapse - Axon terminal - Synaptic cleft - Neurotransmitter - Agonist (mimics a response/neurotransmitter) - Antagonist (blocks a response/neurotransmitter) - Blood brain barrier

    The synapse - The synapse is a small gap between neurons (or between a neuron and a certain cell-like a muscle) - When an action potential reaches the axon terminal it releases a neurotransmitter (chemical sort of messenger) into the synaptic cleft - The postsynaptic (neuron receiving the neurotransmitter) neuron then receives the neurotransmitter and creates another graded potential. - Presynaptic neuron fires - vesicles burst - eject contents into synaptic cleft - neurotransmitter molecules diffuse across cleft - molecules latch on to (lock and key) membrane of post-synaptic cells - this causes certain ion channels of the post-synaptic membrane to open/close - actions a graded potential/action potential.

    How can drugs have an effect? There are 5 main stages when drugs can have an effect 1. Synthesis​: cell has to make neurotransmitter (drugs can affect how well the cell creates/synthesizes the NT) 2. Storage:​ in the presynaptic cleft ( drugs can increase/decrease storage capabilities of cell.) 3. Release: ​vesicles unloading into the synaptic cleft ( drugs can increase/decrease release) 4. Blinding:​ to receptors (can be blocked by the drug that 'plugs' the receptor) 5. Re-uptake: ​neurotransmitter reuptake (drugs can increase/decrease how much of NT is sucked back up during reuptake)

    Synaptic reuptake: - Neurotransmitters are inactivated shortly after they are discharged. They have to be removed from the cleft somehow. There are three main ways re-uptake occurs

    1. Broken down in the cleft by enzymes (e.g. acetylcholine by AChE)
    2. Re-uptake into nerve terminals by re-uptake transporters, which can then be repackaged into synaptic vesicles.
    3. Re-uptake into glia (e.g. astrocytes) by re-uptake transporters

    Agonists vs Antagonists? - Agonists - Mimic a response/neurotransmitter - Does this by binding to postsynaptic receptors and activating or increasing the neurotransmitter effects - Does this by blocking the reuptake of the neurotransmitter - Does this by counteracting the cleanup enzyme that breaks down the transmitter

    • Antagonists
    • Prevent a response/effect of a neurotransmitter
    • Does this by blocking the release of neurotransmitter
    • Does this by destroying neurotransmitter in the synapse
    • Does this by blocking the binding of a neurotransmitter to the postsynaptic cell

    Schizophrenia - Typically, late adolescent onset diagnosis - Affects -1% of the population - Thought process disorder - The brain is oversensitive to the neurotransmitter dopamine - Positive symptoms (these are any symptoms that are added behaviours you don't really see in healthy individuals) - Delusions - Hallucinations - Disordered thinking/unusual behaviours - Negative symptoms (these are symptoms that seem like a loss in normal behaviors that you would see in healthy individuals - Blunted affect/flattened emotional response - Lack of 'normal' speech and thought - Apathy - not really caring what people think of you - Anhedonia - not interested in pleasurable activities - Cognitive symptoms (thinking) - Inability to think logically - Working memory deficits - Inability to pay attention - Treatment is generally to reduce dopamine transmission in the synapses (think a dopamine antagonist) however some treatments can cause Parkinson-like symptoms

    Chapter 3: Reflexes and Memory

    What do we need to know about reflexes? - Reflexes are an involuntary response to a stimulus, - Reflexes are stereotyped, subconscious and unlearned - The purpose of a reflex is to free up the brain, so it is able to do other things.

    Two main types of reflexes Monosynaptic reflex ● 2 neurons, 1 synapse (sensory neuron from muscle to spine to muscle) ● Designed to allow for posture change when load on muscles changes ● The order is as follows: muscles stretch, stretch receptors activated, signal to spinal cord, crosses mono-synaptic (one synapse) synapse, contraction of muscle. ● Example: the knee jerk reflex

    Polysynaptic reflex ● More than one synapse (sensory neuron from muscle to spine, motor neuron from the spine to muscle AND an interneuron from the same sensory neuron, to ANOTHER motor neuron to the antagonistic muscle) ● To walk smoothly you need to have the antagonistic muscle relax ● Therefore, a second motor neuron innervates the antagonist muscle ● Same as above but there is an INTERNEURON, this is INHIBITORY and doesn't send messages outside of the spinal cord ● Example: the withdrawal reflex

    • Memory is stored as a pattern of activity within the brain. Massive networks of neurons and millions of synapses within the brain memory will allow us to recall past events with ease.
    • Experiences are stored as patterns of activity in networks of neurons
    • How do you store that experience as a memory?
    • The brain will store it and reproduce it at a later time. A particular pattern is remembered by the strength of the synapse. The storage is in the strength of the synapse.
    • An increase in synaptic strength could be mediated by:
    • An increase in neurotransmitter release
    • An increase in postsynaptic response
    • An increase in synaptic connections between neurons.

    What do we know about synapses and synaptic strength? - Each time we recall a memory, this strengthens the synapses already in the brain

    • Studied by putting rats into either an impoverished condition, social condition or enriched condition. They found rats in the enriched condition had a greater number of synapses per neuron than the other two conditions. They concluded that memory formations change brain structure, therefore changing synaptic connectivity. Therefore changes in synapses=better memory and learning capabilities.

    What does memory involve? - Memory has three main stages

    Encoding ● The conversion of information into a form that can be stored in memory ● AKA acquisition ● This requires you to think and engage with the material/even/whatever is going on and then the product of this engagement is stored in memory. ● Two different types of processing can occur ● Deep processing. Approaching memorization by focusing on the meaning of the information ● Shallow processing. Approaching memorization by focusing on the superficial characteristics of the information (e.g. the sound)

    Storage ● The creation of a trace of this information within the nervous system ● The trace is a change in synaptic strength. ● This doesn't happen instantly, it takes a while for the memory trace to be created. ● A period of time is needed after a new experience for it to become established in memory ● During this time, memory consolidation occurs ● Memory consolidation results in a more permanent or robust memory that can then remain in storage in LTM. ● The process in the storage requires the creation of new proteins so it can be disrupted by chemical manipulations that hinder or block protein synthesis.

    Retrieval ● An attempt to find and recover a memory trace ● Two different types of retrieval ● Recall. No cues needed ● Recognition. Cues required for memory ● Retrieval failure can result in the tip of the tongue effect. ● A retrieval clue is a hint/signal that helps you recall something ● A retrieval path links bits of info together so people can locate them in memory

    Amnesia and H.M H.M ● H.M lost his memory after a medial temporal lobectomy in attempt to fix the many seizures he had after falling off his bike as a child. ● The result of the surgery was anterograde amnesia (however he also suffered retrograde amnesia of the few years prior to the surgery - likely due to the seizures or the drugs during the surgery but probably not due to the surgery itself.) ● Intact skill learning

    • Anterograde amnesia​ = can't create another memory
    • Remembers past LTM but can't create new LTM.
    • Retrograde amnesia ​= can't remember old memories
    • Can form new LTM but can’t remember old LTM. Measured with the famous faces test.

    Craik and Watkins (1973) ● Once again tested the importance of rehearsal. ● Introduced a distinction between maintenance rehearsal and elaborative rehearsal. ● Maintenance rehearsal. Mechanical repetition of material without thinking about its meaning or patterns ● Elaborative rehearsal. Thinking about the meaning of the context. Relating the content to other things ● Found that maintenance rehearsal was not useful in creating new memories ● Exposure is not enough to create memories ● The deeper the information is processed the better it will be remembered. ● Elaborative rehearsal is most important in establishing memory in the LTM.

    Baddeley and Hitch (1974) ● Argue that the stage theory of memory/multistore model is too simple ● Replaced STM with working memory (which actually processes information -STM was thought to have very little processing. ● Conducted an experiment that had both auditory and visual stimuli. ● Concluded that working memory has different systems for different types of info that might come into the brain for processing

    The central executive ● Allocates info to the VS and PL and relates them to the LTM store ● Is thought of as the working force of the working memory ● Does some tasks e.g. problem solving or basic math ● Having a visual system and a verbal system would make sense as it means we can do a visual and verbal task at the same time e.g. having a conversation while walking or driving

    Visuospatial sketchpad (inner eye) ● Stores info in visual or spatial form e.g. able to walk around a room without bumping into different objects as we are able to relate them to each other and ourselves ● Allows you to answer questions such as &quothow many rooms are in your house?&quot you can visualize it and answer

    Phonological loop ● Stores info in speech form ● E.g. being able to remember a phone number - we repeat the number over and over again to ourselves this occurs in a phonological loop ● Two main parts

    • Phonological store. Speech perception e.g. spoken words are held here for a couple of seconds
    • Articulatory control process. Speech production e.g. allows you to rehearse and store info and produce sound.

    How can we improve memory? ● Method of Loci. Shopping list associated with a well-known route. ● Visual imagery. Associating words with an image

    A hierarchy of memory types Implicit Memory ● Memory that is not available to consciousness e.g. language use ● You can demonstrate implicit memory through an indirect test e.g. implicit association test tests people involuntary associations between mental depictions of objects - relating plate and cup rather than plate and lawn mower ● Explicit/declarative memory ● Refers to memories that are available to consciousness (two types of explicit memory are episodic and semantic) Episodic memory ● Particular to a time and place e.g. memory of your 8th birthday party at chipmunks ● In temporal lobe amnesia episodic memory is damaged (can't remember what you did yesterday afternoon.) ● Semantic memory ● Knowledge memory of facts (e.g. definition of 'affect') - not tied to specific time or place ● In temporal lobe amnesia, semantic memory is INTACT (can still remember the currency used in New Zealand)

    Key Terms: ● Sleep-wake cycle ● SCN ● Circadian rhythm ● Recuperation/Repair Theory ● EEG ● EOG ● EMG ● REM ● SWS ● SWR'S

    The sleep-wake cycle ● The sleep-wake cycle is a circadian rhythm (a cycle controlled by your brain that allows you to go through periods of both tiredness and alertness throughout each day) ● There is a difference in brain activity during the day and when asleep at night ● The brain structure involved in sleep is Suprachiasmatic Nucleus of the hypothalamus (the SCN) ● The SCN responds to the blue light that is received through the optic nerve (which is why it is beneficial to avoid blue light before bed) ● Light is the main indicator (aka zeitgeber) in 'sleep time' (e.g. when its time for your body to start going into sleep mode there are other things involved too, for example: food, exercise etc.) ● The pineal gland is also involved- releasing melatonin (an important hormone involved with sleep) ● Generally, each sleep cycle is 90 minutes: this is when a person will go from SWS to REM sleep. The amount of time spent in REM increases throughout the night (with approx 5 REM cycles per night) ● Total amount spent sleeping decreases consistently with age

    Resetting the circadian rhythm There are just a few facts you need to remember about this. ● The free running period of a circadian rhythm is approx. 25 hours ● This is quite evident in shift workers. You can also use this knowledge to your advantage when travelling. To avoid jetlag try and shift your sleep cycle before you travel, its also easier to travel west rather than east. ● You can try and speed up the shift of the cycle with things like exercise and supplements like melatonin ● Its easier to phase delay (wake up later) than phase advance (wake up earlier)

    Why do we need sleep? ● To clear out cellular waste products from metabolic activity throughout the day ● For production of growth hormones ● For maintenance of synapses ● For consolidation of memories ● PERHAPS - to help digest food - evolutionary wise this would make sense, spend time in a safe place and sleep to allow this. ● PERHAPS - because vision is poor at night - evolutionary wise this would mean humans would be easy prey at night, and they can't complete important things like hunt for food/mates ● PERHAPS to conserve energy ● If you look at animals, the amount of sleep they have per day seems to relate to how much time they spend hunting for food, and how vulnerable they are to attack (e.g. tiger seems to sleep a lot more than giraffe!)

    Recuperation/repair theory - Recuperation/repair theory predicts that being deprived of sleep will result in an individual having both physiological and psychological instabilities, i.e. that the longer a person is sleep deprived the worse this will get, but that with sleep the person can recover - What are the results of sleep deprivation - Peter Tripp (1959) 201 hours with no sleep and went into psychosis - Randy Gardner (1964) 264 hours (about 11 days) impairments in attention, motivation and cognition - In animals: After two weeks of no sleep rats were unable to maintain their body temperature, lost weight and were more prone to illness. After four weeks, several rats died - In shift work: more prone to diabetes, being overweight, mood disorders and link to cancer

    How to measure physiological changes during sleep? 1. Electroencephalogram (EEG) - measures brain electrical activity 2. Electrooculogram (EOG) - measures eye movement 3. Electromyogram (EMG) - measures muscle tensions

    What are sharp-wave ripples? ● SWR's are seen on an EEG during sleep, they are generated by the hippocampus and almost like very quick bursts of activity ● SWR's which occur during sleep can actually mimic the same firing patterns that happened when an individual was awake ● This is why it is thought perhaps SWR's help with memory consolidation (remember increased firing of synapses = better memory) ● SWR'S occur during SWS ● When SWR's occur, the neocortex also is making 'sleep spindles' (sudden bursts of activity also thought to be critical for learning)

    Summary: ● An overview of sleep, the sleep-wake cycle, REM, SWS and common beliefs about dreams. A pretty brief part of the first block of lecture material, but there is always questions on this in the exam.

    Chapter 5: Sensation and Perception and the Eye

    Key terms: ● Sensation ● Perception ● Cornea ● Anterior chamber ● Sclera ● Choroid ● Retina ● Photoreceptors ● RGS's ● BC's ● Accommodation ● Blindspot ● Cataracts

    Sensation and perception Sensation ● The stimulus detection process by which our sense organs respond to and translate environmental stimuli into nerve impulses Perception ● The active process of organizing this stimulus input and giving it meaning

    Sensation is lower-order processing (think the sensory system) whereas perception is a higher-order processing event (as you actually have to use the brain to organize/understand the info that you have experienced)

    Neural Implementation Step 1: the sensory organs experience something and absorb energy Step 2: this energy is transformed into a neural signal (action potential) Step 3: this neural signal is then sent throughout the brain for further processing

    The structure of the eye The outer layer: ● Cornea: thin layer over the eye which focuses the image by allowing light into the eye/also for protection. Is also transparent and avascular which is important for light rays to enter the eye. ● Anterior chamber: contains fluid - aqueous humor which supplies nutrients, removes waste via blood vessels ● Sclera: white outer layer of the eyeball used for protection ● Extraocular muscles: allow the eye to move The middle layer: ● Choroid: the inner lining of the sclera, contains blood vessels to keep eye alive The inner layer: ● Lens: located behind the iris, held in place by ligaments, involved in the fine tuning of light rays. Vitally important for focusing the eye. ● Iris: coloured part of the eye which contains a muscle that controls the size of the pupil ● Pupil: hole at center of retina, determines how much light enters the eye ● Vitreous chamber: back side of the lens containing vitreous humous and provides nutrients to other parts of the eye that the chambers and blood vessels don’t reach ● Retina: transforms received light to neural activity, has many layers of nervous tissue containing rods/cones (we'll get to these). The retina is made up of photoreceptors (rods and cones), bipolar cells and ganglion cells. ● Optic nerve: the axon of the retinal ganglion cells

    Photoreceptors (rods and cones) - Photoreceptors include rods and cones and are the outer layer of the retina - Photoreceptors transform light into neural activity

    Cones: ● Colour ● Daytime use ● High resolution ● High number of cones in the fovea (small pit in the middle of the retina that allows light to fall directly on cones = very clear vision) ● 6 million cones (1 million RGC's) ● Three different types of cones which respond to different wavelengths

    Crossed pathways of the visual system

    As you can see, the nasal side of the retina (close to the nose) crosses over to the other side of the brain. But the nasal side of the retina actually looks at the other sides of the visual fields, whereas the temporal side of the retina, does not cross over at the optic chiasm but remains in the same hemisphere and looks at the inner visual field.

    The pathway from eye to brain Lateral geniculate nucleus (LGN) LGN cells are similar to RGC's in that they also respond well to different size spots of light (however their function is totally different)

    • V1 cells respond well to lines, but different cells within V1 respond to different orientations of lines (so they don’t all like the same thing)
    • Beyond V1, cells generally fire more for novel/complex/interesting shapes! The firing of cells will be less intense when it is a familiar object

    Accommodation and cataracts ● Accommodation = quick reshaping of the lens to see things in focus e.g. look at something 100m away then look at something 1m away ● Cataracts = cloudy vision (generally happens with old age) ● With cataracts, the lens is taken out and replaced with a new one, but even with all scientific advances the lens cannot accommodate as well as a well-functioning original lens ● Congenital cataracts = cataracts from birth. Back in the day they would remove the lens and be unable to replace it, so people were rendered blind. This was because the lens was not present during the critical development stage, which is when, in order for the sensory system to develop perception, the individual must be able to see and receive stimulation or the brain will not wire correctly.

    Blindspot - An area where there are no rods or cones.

    Summary: ● An overview of sensation and perception, the structure of the eye, photoreceptors, bipolar cells, and retinal ganglion cells. You will also be reminded about accommodation and cataracts

    Chapter 6: Review of Brain Organisation and Further Visual

    Key Terms: ● Frontal lobe ● Parietal lobe ● Temporal lobe ● Occipital lobe ● Sulci ● Gyri ● Cortex ● Subcortex ● Retinotopic mapping ● Lateral inhibition ● The Herman grid

    Review of brain organization Frontal lobe ● Front of each hemisphere ● Crucial for many aspects of planning/controlling thoughts/behaviour Parietal lobe ● In both hemispheres, lie in between frontal and occipital lobes ● Receives sensory information ● The 'where' lobe - if the parietal lobe is damaged studies have shown monkeys are not very good at a landmark task (have to pick up an object close to the cylinder each time, regardless of where the cylinder is placed, after several tests the monkey cannot learn this.) Occipital lobe ● Rearmost of each hemisphere ● Processes visual info Temporal lobe ● Both hemispheres lie below the temples ● Crucial for hearing and language use ● Many functions - visual, auditory, memory

    Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

    Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson’s disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about

    for decoding movement from the resting state and about

    for decoding left and right visually cued movements.

    1. Introduction

    A fundamental function of the brain-machine interfaces (BMI) is to decode and interpret the recorded neural potentials to classify the patient’s intentions or intended behaviors. Such information allows for a better understanding of neuronal circuit mechanisms and enables possible development of treatment methods for neurodegenerative disorders [1].

    Deep brain stimulation (DBS) [2–4] is a functional neurosurgical procedure of implanting a miniature medical device to send electronic signals to certain parts of the brain such as subthalamic nucleus (STN) or globus pallidus interna (GPi) in Basal Ganglia (BG) for treatment of movement disorders such as Parkinson’s disease (PD) or Dystonia. At the same time, DBS devices can be considered for BMI design and they are able to record the neurosignals called local field potentials (LFPs) [5–7] for body movement prediction or interpretation. Deep brain LFPs represent the aggregation activities of a large population of local synchronous neurons [5] and can provide neuronal information with better quality (i.e., high SNR) and greater stability over time compared with single-unit activity (SUA). The acquired LFPs from implanted DBS macroelectrodes can be used by researchers and clinicians to investigate on functioning of the Basal Ganglia in motor control [8] for better understanding and more effective treatments of movement disorders [9]. Deep brain LFPs reflect synchronized, subthreshold currents generated in the somata and dendrites of local neuronal elements [10] and they can be subdivided into a number of frequency bands including delta (0–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–32 Hz), gamma (31–200 Hz), and high-frequency (>200 Hz) [9] bands. During human body movements, the frequency of the LFP signals can be as high as 300 Hz [7] and is likely to vary due to a varied degree of behavioral and disease correlation. For example, in case when self-paced (voluntary), externally cued movements or any specified action is intended to be performed, the frequency-dependent event-related synchronization (ERS) and event-related desynchronization (ERD) can be found in various LFP bands recorded in bilateral STNs and/or GPIs [5, 10], which suggests that these oscillations may be related to the preparations of motor response.

    With the analysis of intra-operative LFP recordings, it has been found that the frequencies of the synchronized oscillatory activities generally belong to one of two different bands for PD patients withdrawn from dopaminergic therapy [10]. The first band contains activity frequencies (3–12 Hz) of Parkinsonian rest and action tremor, but the signal in this band is neither consistent nor a strong feature of LFPs. However, the second band, called beta band (13–32 Hz), is the frequency range representative of LFP oscillations. This band is antikinetic in nature and is manifested in single-unit activity [10]. Furthermore, for PD patients, the improvement in bradykinesia and rigidity with the subsequent dopaminergic therapy was shown to be correlated to the signal magnitude change in the beta band [9]. However, for PD patients, the oscillatory characteristics of beta frequency band are augmented to such an extent that they dominate over motor commands used for initializing voluntary movements, leading to movement disorders [13]. The most consistent of beta band activities can be found in the untreated, hypodopaminergic Parkinsonian state [14–16]. Recent study also substantiated that the strong signal components in beta frequency band were observed in LFPs recorded from the GPI of PD patients, whereas, for Dystonia patients, the signal in the same frequency band was much less salient [9]. For Essential Tremor (ET), the tremor signals are consistently in the frequency range of 8–27 Hz. For cervical Dystonia, the frequency ranges of 4–10, 11–30, and 65–85 Hz of LFPs are highly correlated to sternocleidomastoid muscle EMG signal frequencies [9]. In addition, ERD in beta band (10–24 Hz) was observed during human movement initiation process and ERS during cessation of movement [9]. At rest and during “OFF” medication Parkinsonian state, alpha (8–12) Hz and beta (13–32) Hz oscillatory activities dominate in the LFP frequency spectra, while they are drastically reduced during “ON” medication state [7]. Moreover, during “OFF” levodopa, the activity in gamma band increases bilaterally during active movement [9] and high-frequency oscillations (HFO) (300–350 Hz) may heighten. In addition, it was also reported that, during “ON” and “OFF’ medication states in PD, the extent of power in the frequency band of 4–10 Hz is lower in contrast to Dystonia patients [9]. Although the oscillations in gamma band (>70 Hz) in LFPs that is correlated to human movement (prokinetic) were suppressed [13] or absent in PD patients, during the “ON” medication state, the synchronized oscillatory activity may occur in the STNs and GPIs. Although the evidence suggests that these frequency activities would increase when the body changes from rest to movement, the activities above 65 Hz appear to be an unreliable LFP feature for PD patients [10].

    Basal Ganglia STNs activity can be modulated, while patient intends to perform a specified action or watches visual images of movements [17]. Such intended movements are responsible for generating ERS and ERD in Basal Ganglia which are similar in frequency and time to those during actual voluntary movement [1]. Although the differences in the midst of contra- and ipsi-lateral movement-related oscillatory changes in the STNs have been unknown, some studies suggest that there may not be substantial differences. However, it was also reported recently that, during wrist movement tasks, both contra- and ipsi-lateral ERS were observed in the gamma frequency band [7] but event-related desynchronization (ERD) was found in the low-beta frequency band (

    Therefore, multiple frequency-dependent oscillations in motor cortex and BG are directly related to the process of action making, preparations, executions, and imaginations of movements [7]. Recent experimental results showed that, based on distinct oscillations of LFPs, self-paced hand movements can be predicted using a pattern recognition algorithm [18]. The result indicates that LFP activity is directly or indirectly involved in the process of motor preparation. In addition, it is found that the LFPs can be used to infer substantial information about specific types of arm movement parameters such as distance, speed, and directions for motor disorder patients [19, 20]. A recent study showed that movement in eight directions can be decoded with the best recognition rate of up to 92% using the spatial patterns of LFPs in premotor and primary motor areas [19].

    Some studies have been conducted to find the coherence and causality between cortex and hand movement. In one study, it was found that noteworthy coherence only exists between the human sensorimotor cortex and contralateral hand and forearm muscles. However, no existence of coherence was found in sensorimotor cortices or any ipsi-lateral hand and forearm muscle [21]. In another study, it was shown that voluntary movement can be decoded up to

    % using causal strength of LFP signal features computed on neural synchronization of bilateral STNs or GPIs and utilizing bivariate Granger Causality [1]. Additionally, it was found that left and right hand movements are associated with different spatiotemporal patterns of movement-related synchronization and de-synchronization [22]. Therefore, motor control or bilateral coordination can be predicted by decoding movement intention from Basal Ganglia neural activities for left and right hands [1, 7, 12]. These research findings have further demonstrated that LFPs during onset of movement contain supportive information that may advance our knowledge towards reliable movement decoding strategies for neuroprosthetic device developments, diagnostic assessments, and possible treatment of some chronic neurological disorders. For instance, early prediction of onset of tremor of PD patients may provide the possibility of constructing an adaptive therapeutic intervention mechanism in using DBS for optimal neuromodulation effects [3].

    Hence, the prediction and classification of human body movements can be achieved by decoding the recorded BG LFP signals using pattern recognition algorithms. In this paper, we have developed an innovative neural network (NN) based ensemble classifier for effectively decoding the LFP signals recorded from sequential occurrence of movements and identifying whether the movement is left- or right-sided visually cued in an automated and systematic fashion.

    Artificial neural networks (ANNs) [23] are one of the most effective and commonly used machine learning algorithms. However, different types of ANN algorithms possess various advantages and disadvantages in classification. For instance, the FBANN, that is, multi-layer perceptron (MLP), is relatively efficient in optimization or classification with limited training data but tends to be stuck in the local minima and provides less satisfactory classification results [24]. On the other hand, RBFNN could find the global minimum [25] but requires much larger dataset to train. Alternatively, PNN, derived from the Bayes rule and kernel Fisher discriminant, is more accurate than MLP networks and insensitive to outliers in training data [26]. However, PNN needs more training data and is slower than MLP networks in classification. Therefore, it is highly preferable if we can design an ensemble classifier that uses all of the neural networks as the base classifiers for their collective advantages. The ensemble classifier would contain all the advantages of the above-mentioned networks for better activity decoding and classification using LFP dataset. Also, to get robust and consistent movement in decoding performance, we develop a decision fusion algorithm based on the majority voting strategy to combine the classification results from three individual neural networks. The majority voting is simple, intuitive, and effective ensemble approach for improving classification performance [27, 28]. Recently, it has been shown that when seven base classifiers were used in five different ensemble strategies, including majority voting, Bayesian, logistic regression, fuzzy integral, and neural network, the majority voting strategy proved to be as effective as any other algorithm in improving overall classification performance for the dataset provided [28]. We believe that identifying visually cued voluntary movements by decoding oscillatory characteristics of LFP activity may provide ways of developing more advanced neural interface systems such as BCIs and BMIs to enhance our understandings of the underlying process of movements and its important implications in STNs or GPIs for controlling movement activities.

    2. Experimental Framework and Data Acquisition (DAQ) System

    The LFP datasets used in training and testing for movement recognition were recorded through the DBS devices from the patients with Parkinson’s diseases (PD) or Dystonia. The circumstances of the data acquisition are described in detail in this section.

    2.1. Patient Details

    In this work, a total of twelve Parkinson’s disease or Dystonia patients (7 males and 5 females) with their ages ranging between 23 and 72 years ( , mean ± 1SD) were recruited. Each patient underwent bilateral implantation of deep brain stimulation (DBS) electrodes in the STN or GPI for therapeutic stimulation to provide the LFP signals for recording. Their disease-suffering durations were between 3 and 38 years (

    , mean ± 1SD). The corresponding demographics are summarized in Table 1. The LFP data collection was approved by the local research ethics board at Oxford University. All participants provided written consent prior to this study.

    2.2. Deep Brain Stimulation (DBS) Electrode Setup

    The DBS macroelectrode (model: 3387, manufacturer: Medtronic Neurological Division, Minneapolis, USA) was implanted bilaterally in the left and right STNs or GPIs for treatment of the patients with Parkinson’s disease or Dystonia. The macroelectrode consists of four platinum-iridium cylindrical surfaces (diameter: 1.27 mm, length: 1.5 mm, and center to center spacing: 2 mm contact-0 is the most caudal and contact-3 is the most rostral). Macroelectrodes were inserted after STN and had been identified by using ventriculography and pre-operative magnetic resonance imaging (MRI). Stimulation spots were chosen as the electrode positions, where lessening in Parkinsonian symptoms occurred during intra-operative electrical stimulation and the matching is confirmed by examining the post-operative MRI scan or the fused images of pre-implantation MRI with post-implantation CT.

    2.3. Movement Activities of the Patients

    During LFP recording from STNs (Figure 1) or GPIs, all subjects were instructed to do a finger pressing task in a random order with a short resting period between tasks. Each subject was seated 60 cm (approx.) away from the experimental computer screen. After that, prior to each motor task, they were instructed to keep their left or right index fingers on the distinct keys on the left or right standard keyboard. In addition, all the patients were asked to look at a 10 mm cross that was repetitively displayed in the center of the screen and letter A (height: 8 mm width: 7 mm) on the screen for the duration of 400 ms instantly to the left or right central cross. It was the indication signal to the patients to move the finger. The interval of cues and laterality were provided randomly in the experiment.

    2.4. LFP Signal Acquisition from Patients

    The LFP signals of twelve patients were recorded at STNs and GPIs for 4–6 days via externalized electrode leads post-operatively after all the patients had been kept “OFF” medication overnight and high-frequency stimulation pulses were completely turned “OFF.” Using MRI, the DBS lead contacts at STNs or GPIs to record LFP signals on both sides were confirmed. Three adjacent pairs consisting of 4 contacts named 0, 1, 2, and 3, respectively (pair positions are 0-1, 1-2, and 2-3), were used to record LFPs in the bipolar signal form and bilaterally. Usually, the bipolar configuration was used to provide “common mode rejection” to far-field activity signals against common mode noise contamination. If DBS stimulation and activity recording are conducted simultaneously, the LFP signal recording can be interfered by the DBS stimulation pulses, leading to inaccurate recording and decoding results. In this experimental setup, we recorded the LFP signals well before the stimulation started to avoid any possible interference of the simulation pulses to activity recording. DBS macroelectrode pairs were chosen for better therapeutic effects and anatomical structures. After that, the segments of the recorded signal containing erroneous, premature, or no responses were deliberately discarded from the datasets. The number of trials had to be kept at minimum to minimize the stress during the experiment imposed on the PD/Dystonia patients. In the experimental session, trials (mean ± 1SD) consisting of minimum 56 and maximum 202 trials across all subjects were employed in the movement decoding process. In addition, for most of the patients, the number of trials is unbalanced for each class. The average number of trials of each class is

    (mean ± 1SD) with a minimum of 25 trials and maximum of 113 trials and the average difference between the classes across all the subjects is 14.2% ± 19.0 trials (with a minimum of 1.2% and a maximum of 57.6%). The DBS surgery was only warranted if the patient had exhibited motion-related dysfunction in postural control, gait, and locomotion in addition to usual motor symptoms such as tremor, rigidity, and bradykinesia. Under these circumstances, there will be always challenges with the amount of data with sufficient neuronal information to be collected therefore to develop an analysis method that does not rely on a large number of trials is of paramount importance. However, for avoiding rapid repetitive movements and obtaining valid ranges of inter-movement data, the LFP signals obtained outside the time range between 1 s and 5 s during a movement were excluded from the datasets. The contact pair (from bipolar mode: 0-1, 1-2, and 2-3) in the Basal Ganglia were chosen for analysis and showed greatest percentage of beta (β) band (13–32 Hz) modulation due to the movement in contrast to the amplitude of β modulation during the baseline activity period occurring 1-2 seconds before the onset of motor response. The LFP information obtained from the available contact pairs of each electrode would be highly correlated and therefore only one contact pair of each electrode was used for data recording and analysis. In the recording scheme, CED 1902 amplifiers (×10,000) were employed to amplify the initial signals recorded at the DBS contacts. With tripolar configurations (active-common-reference), surface EMGs were recorded using disposable adhesive Ag/AgCl electrodes (H27P, Kendall-LTP, MA, USA). Based on the recorded EMGs from the index finger, the onset of motor response and other voluntary and involuntary movements were determined by timing of the key presses as registration of motor response. The movement-related artifacts due to equipment lead were carefully identified and the recordings containing excessive noises were excluded from analysis. Contaminated trials with artifact were also removed. In addition, noise of the recorded data related to patients’ movement were avoided as much as possible by instructing patients to stay in steady condition during each session of recordings. In the recorded EMGs, rest and movement conditions were defined as follows: “rest” is defined as no or little hypertonic bursts, “voluntary movements” are defined as regular pulses with a duration of tens of milliseconds, and “uncontrolled contractions” are defined as phasic spasm over seconds. The initial signals were amplified using isolated CED 1902 amplifiers (×10,000 for LFPs and ×1000 for EMGs), low-pass filtered with a cut-off frequency of 500 Hz, and then digitized using 12-bit CED 1401 mark II with a sampling rate of 2000 Hz. Subsequently, a custom written program in SPIKE 2 (Cambridge Electronic Design (CED), Cambridge, UK) software was used for recording, online monitoring, and storing the digitized data in the hard drive. Variations of instantaneous magnitude and frequency for both LFPs and EMGs were compared to find correlations between them during movement activities.

    2.5. Preprocessing of STN’s LFP Signals

    For removing high-frequency noise and artifacts, a low-pass type-I Chebyshev filter (zero phase shifting and cut-off frequency 90 Hz) was applied to the STN’s LFP signals. A notch filter at 50 Hz was further applied to the processed signals to remove the single-frequency noise associated with the power supplies. Then the LFP datasets were digitally resampled at 256 Hz prior to feature extraction and classification processing.

    3. Methodology of Feature Extraction of LFP Signals Using Wavelet Packet Transform (WPT) and Hilbert Transform (HT)

    To carry out the identification of finger movements from the LFP data, we used wavelet packet transform (WPT) and Hilbert Transform (HT) to extract the LFP signal features from different frequency bands in the frequency range from 0 to 90 Hz. For non-stationary biosignals such as LFPs, WPT is a better alternative as a data analysis tool than STFT or standard DWT in extracting relevant signal features for pattern recognition in the time-frequency domain [29].

    WPT can decompose both approximation and detail spaces into further subbands with functionally distinct scales in a balanced binary tree and has ability to localize any specific information of interest as compared to DWT [30, 31]. In carrying out the WPT at decomposition scale of 5, the discrete Meyer wavelet (demy) was selected and applied to the LFP data to generate different multi-resolution coefficients. The WPT coefficients are obtained by recursively filtering out the coefficients generated in the previous stage with lower resolutions to compute the WPT coefficients at current scale.

    After completion of the WPT processing, we segmented a 4-second time window from each frequency band for LFP’s left and right clicking event tasks at each motor response registration (Figures 2(a) and 2(b)). Likewise, we can segment the resting activity into a total of 2-second time windows during each stimulus registration. The signal envelope in each frequency band of the reconstructed signal was computed by using the Hilbert Transform (HT) [32] and the signal features were extracted based on the power of each frequency band. From Figure 2(c), it can be seen that event-related synchronization and desynchronization happened in all frequency bands but visible amplitude decrement was found in β band at the left and right STNs or GPIs. However, at the event onset, the signal amplitude in the δ band was quite large compared to those in other bands.