A pinboard by
Jyoti Singh Kirar

Ph.D Scholar, Jawaharlal Nehru University


Algorithm development for classification of motor imagery tasks

A Brain Computer Interface (BCI) is a technique that interprets neuronal activity to derive user commands and thus creates a direct communication pathway between a brain and a device without involving the brain's conventional output pathways such as muscles and peripheral nerves. The main purpose of a BCI is to help a person with severe motor disabilities to control devices such as computers, speech synthesizers, assistive appliances. An increased attention is observed for an EEG based motor imagery paradigm which involves thinking or imagination of a specific body part which induces variations in rhythmic activity recorded over sensory motor cortex of brain scalp called sensory-motor rhythms. These variations can be captured using scalp EEG and are useful in classification of motor imagery tasks.


Composite kernel support vector machine based performance enhancement of brain computer interface in conjunction with spatial filter

Abstract: Publication date: March 2017 Source:Biomedical Signal Processing and Control, Volume 33 Author(s): Jyoti Singh Kirar, R.K. Agrawal For Motor imagery Brain Computer interface, a large number of electrodes are placed on the scalp to acquire EEG signals. However, the available number of samples from a subject’s EEG is very less. In such a situation, learning models which use spatial features obtained using common spatial pattern (CSP) method suffer from overfitting and leads to degradation in performance. In this paper, we propose a novel three phase method CKSCSP which automatically determines a minimal set of relevant electrodes along with their spatial location to achieve enhanced performance to distinguish motor imagery tasks for a given subject. In the first phase, electrodes placed on brain scalp are divided among five major regions (lobes) viz. frontal, central, temporal, parietal and occipital based on anatomy of brain. In the second phase, stationary-CSP is used to extract features from each region separately. Stationary-CSP will handle the non-stationarity of EEG. In the third phase, recursive feature elimination in conjunction with composite kernel support vector machine is used to rank brain regions according to their relevance to distinguish two motor-imagery tasks. Experimental results on publically available datasets demonstrate superior performance of the proposed method in comparison to CSP and stationary CSP. Also, Friedman statistical test demonstrates that the proposed method CKSCSP (μ≠0) outperforms existing methods.

Pub.: 02 Dec '16, Pinned: 29 Jun '17

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

Abstract: Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.

Pub.: 15 Jun '17, Pinned: 29 Jun '17