A Hybrid Approach for Extracting EMG signals by Filtering EEG Data for IoT Applications for Immobile Persons

Wireless Personal Communications - Brain Computer interface (BCI) is an emerging technology which empowers human to regulate the computer or other electronic gadgets with brain signals. This paper...     

Lie detection using extreme learning machine: A concealed information test based on short-time Fourier transform and binary bat optimization using a novel fitness function

Lie detection is one of the major challenges that is being faced by the forensic sciences. Identification of lie on the basis of a person's mental behavior is a tedious task. Brain-computer interface is one such medium which provides a solution to this problem by displaying visual stimuli and recording subject's brain responses. A P300 response is elicited whenever a person comes across a familiar stimuli in a series of rare stimuli. This P300 response is used for the lie detection method. In the proposed concealed information test, acquired signals are preprocessed to discard noise. Then, short-time Fourier transform method is applied to extract features from the preprocessed electroencephalogram signals. To avoid the curse of dimensionality and to reduce computational overhead, binary bat algorithm is applied, which helps in choosing optimal subset of features. The obtained set of features is given as an input to the extreme learning machine classifier for training of guilty and innocent samples. The performance of the system is assessed using 10-fold cross-validation. The resultant accuracy obtained from the proposed lie detection system is 88.3%. The system has provided efficient results in contrast with most of the state-of-the-art lie detection methods.     

EEG Data Classification for Mental State Analysis Using Wavelet Packet Transform and Gaussian Process Classifier

Stress is one of the most common problems that is faced by a majority of the students. Long-term stress can lead to serious health problems, for example, depression, heart disease, anxiety, and sleep disorder. This paper proposes an efficient stress level detection framework to detect the stress in students using Electroencephalogram (EEG) signals. The framework classifies stress into three levels; low stress, medium stress and high stress. In this experiment, EEG data is collected from six subjects by placing two electrodes in the prefrontal region. During each trial, the subject solves arithmetic questions under some time pressure. The EEG data is collected while the subject solves the question. The collected data is pre-processed using a band-pass filter to remove artefacts and appropriate features are extracted through the wavelet packet transform and PyEEG module. ReliefF feature selection method is used to select the best features for classification. The selected feature set is classified into three categories using Gaussian Classification. The proposed framework effectively classifies the level of stress with an accuracy of 94%.