Neuro-Muscular Engineering
Sahar Akbari; Vahid Shalchyan; Mohammad Reza Daliri
Volume 12, Issue 4 , January 2019, , Pages 277-286
Abstract
Neural spike detection is the first step in the analysis of neural action potentials in extracellular recordings. The background noise which mainly originates from a large number of far neuronal units, usually confront with detection of low-amplitude spikes. So far, many scholars have devoted their works ...
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Neural spike detection is the first step in the analysis of neural action potentials in extracellular recordings. The background noise which mainly originates from a large number of far neuronal units, usually confront with detection of low-amplitude spikes. So far, many scholars have devoted their works to this subject and many algorithms have been proposed. In this paper we present an automatic spike detection algorithm for the noise-contaminated extracellular signal. This algorithm consists of four steps: 1- A bandpass filtering and using a differential filter; 2- applying Shannon's energy nonlinear filter; 3- Hilbert transform; and 4- Thresholding of the signal. The proposed method has been compared with five known methods in spike detection. This comparison is done on two simulated datasets and one real data set. The results indicate the superiority of the proposed method for simulated data compared to other methods, which indicates the robustness of the proposed algorithm to the noise. Meanwhile, for real data, it reaches the second place among all six methods. Using Shannon's non-linear energy filter can be an effective way to detect spikes in extracellular signal recordings. The comparison indicates that this method is superior to the commonly known methods for spike detection.
Farnaz Saberpour; Mohsen Parto Dezfouli; Vahid Shalchyan; Mohammad Reza Daliri
Volume 12, Issue 3 , November 2018, , Pages 189-198
Abstract
Neural adaptation is a brain ability which reduces the neural activities in response to a repeated stimulus. In this study, we examined the effect of adaptation on neural decoding. For this purpose, pure tones with different frequency-amplitude combinations were presented randomly in two sequences (usual ...
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Neural adaptation is a brain ability which reduces the neural activities in response to a repeated stimulus. In this study, we examined the effect of adaptation on neural decoding. For this purpose, pure tones with different frequency-amplitude combinations were presented randomly in two sequences (usual and adaptive). During the task, local field potential (LFP) signals were recorded from the primary auditory cortex of fifteen anesthetized rats. In the usual sequence, the stimuli were presented randomly with 50 ms duration and 300 ms interstimulus interval (ISI). Each combination was presented about 25 times. In the adaptive sequence, same as the usual one, stimuli were presented with this difference that one specific frequency (adapter) with the probability of 80% was presented frequently in this sequence. Comparison between decoding accuracy of two sequences allows us to study the effect of adaptation to a specific frequency on neural decoding. First, considering the power spectrum feature in six frequency bands and using LDA (linear discrimination analysis) classifier, the average decoding accuracy of all frequency-pairs were calculated in the usual sequence. Subsequently, the decoding accuracy of frequency-pairs in the adaptive sequence was calculated and compared with the usual sequence. Results show a significant decoding accuracy between different frequency-pairs in beta, gamma, and high-gamma bands (>12 Hz) of local field potential with an accuracy of about 80%. Moreover, we found that adaptation to one frequency of sound decreases the decoding accuracy of neighbor frequencies. This signature was observed in high-frequency gamma and high-gamma activities (30-120 Hz) of LFPs.
Bioelectrics
Amir Soleymankhani; Vahid Shalchyan
Volume 12, Issue 2 , September 2018, , Pages 85-96
Abstract
The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity ...
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The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity of single neurons. Based on the structural properties of the neuron, such as its dendritic tree, and the distance and direction of it relative to the electrode, it can be claimed that the form of its spike waveform is unique and constant. However, spike sorting under low signal-to-noise ratio (SNR) conditions is always accompanied with challenges. A spike sorting algorithm usually consists of three sections including the spike detection, feature extraction, and classification. In this paper, a method based on optimization of continuous wavelet coefficients is presented which is effective in low SNR values. In the proposed method, after the calculation of the parameterized wavelet coefficients, using the Euclidean distance and the area under the receiver operator characteristic curve, the best parameters were chosen to increase the separation of the features, so that a suitable scale was first found with the Euclidean distance criterion and then the translation parameter was obtained with the second criterion. In this research k-means algorithm was used for the clustering as a simple but efficient method. For evaluation, three simulated data sets were made in 9 different SNRs with a modeled background noise. The obtained results from simulated data showed that the optimization of parameters in continuous wavelet transform using the proposed algorithm could effectively improve the spike sorting performance compared to principal component analysis method.
Neuro-Muscular Engineering
Amir Masoud Ahmadi; Sepideh Farakhor Seghinsara; Mohamad Reza Daliri; Vahid Shalchyan
Volume 11, Issue 1 , May 2017, , Pages 83-100
Abstract
The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation ...
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The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation method is provided therapeutic benefits for treatment chronic pain, essential tremor, Parkinson’s disease, major depression, and neurological movement disorder syndrome (dystonia). One area in which advancements have been recently made is in controlling the movement and navigation of animals in a specific pathway. It is important to identify brain targets in order to stimulate appropriate brain regions for all the applications listed above. An animal navigation system based on brain electrical stimulation is used to develop new behavioral models for the aim of creating a platform for interacting with the animal nervous system in the spatial learning task. In the context of animal navigation the electrical stimulation has been used either as creating virtual sensation for movement guidance or virtual reward for movement motivation. In this paper, different approaches and techniques of brain electrical stimulation for this application has been reviewed.
Neuro-Muscular Engineering
Hesam Moradkhani; Vahid Shalchyan
Volume 10, Issue 4 , January 2017, , Pages 325-337
Abstract
P300 Speller as a most commonly used brain–computer interface (BCI) has been able to provide simple communication capabilities for people with severe motor or speech disabilities in order to have a better interaction with the outer world over the past years. Checker-board paradigm introduced by ...
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P300 Speller as a most commonly used brain–computer interface (BCI) has been able to provide simple communication capabilities for people with severe motor or speech disabilities in order to have a better interaction with the outer world over the past years. Checker-board paradigm introduced by Townsend et al. [1] is one of the most practical alternatives for row-column paradigm, enhancing the performance of the speller by preventing row-column induced errors. In this study, we investigated the effect of substituting presentation of an emoji stimulus instead of flashing the characters in the performance of a checker-board P-300 speller. The performance of the proposed paradigm was evaluated and compared to the traditional stimuli in checker-board paradigm in an online experiment over ten healthy subjects. For each paradigm, the recorded data from an offline session was used to calibrate the speller classifier; and consequently, the classification accuracy was calculated over online sessions. The proposed paradigm, showed 14% enhancement in classification accuracy with respect to the checker-board paradigm. The results of this study obviously showed that the stimuli obtained by presenting emoji instead of character flashing, effectively improved the speller classification accuracy.
Reza Foodeh; Vahid Shalchyan; Mohammad Reza Daliri
Volume 10, Issue 3 , October 2016, , Pages 267-277
Abstract
Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate ...
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Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate spatial patterns in the electroencephalogram (EEG) signals to discriminate different motor imagery classes. Before applying CSP, Usually EEG signals are filtered out in 8-30 Hz to capture event related desynchronization (ERD) specific frequency rhythms called mu and beta bands. However, this frequency band could be highly subject specific. Therefore, optimizing spectral and spatial filters jointly could improve the classification accuracy. In this paper, we proposed a novel learning algorithm to derive spatial and spectral filters simultaneously using an evolutionary learning algorithm called particle swarm optimization (PSO). Furthermore, we utilized mutual information between extracted features and class labels as a cost function in the learning algorithm. Our simulations on BCI competition IV, dataset 1 reveals that the proposed method significantly outperforms the conventional CSP and filter bank CSP (FBCSP) with two different filter bank architectures.
