Neural Recording And Processing Of The Neural Signals

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Neural Recording and Processing
The most critical element of a Brain Machine Interface (BMI) is the recording and processing of the neural signal. We use an invasive neural signal recording to achieve higher performance of the BMI and to obtain better resolution. We will be recording the neural signal on central sulcus located in the cortex region of the brain. This recording is referred to as Electrocorticography (ECoG). We will be using subdural grid electrodes (surface electrodes) with 48 contact points spanning across the upper limb region of the central sulcus. The signal will be picked up by the electrodes and it will be transferred to the signal processing unit through the encapsulated leads. The leads will be connecting the
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The signal gets amplified, filtered and then get digitised. The digitised data is fed into the decoder.
Signal Decoding
We intend to design a signal decoder as an ASIC. It will perform the decoding of neural signals and predict the brain’s intention to perform arm movements. Though we use multiple electrodes to record the neural signal, the number of features that can be extracted from the recorded signal is very less. The neurons in the brain perform a task by sequential generation of action potentials which are then transmitted to other neurons through the synapse. These action potentials are referred to as spikes.
The first step in decoding is to extract features from the recorded neural signal. The features we will be interested are the spikes. The efficient method to detect spikes is threshold crossing. This is a simple approach where we will setup threshold amplitude and count the number of threshold crossings across a time bin. Whenever a spike or set of spikes cross the threshold value it is detected and assigned to a single or a group of neuron. This method does not affect the decoding performance and has gained acceptance widely. We will adopt the threshold crossing technique to extract features from the neural signal.
We will identify the spikes detected based on their frequency, timing and location. The information found is referred to as rate coding, temporal coding and population coding respectively. We will then classify the spikes based on this
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