Fast and accurate classifier-based brain-computer interface system using single channel EEG data

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

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DEVELOPMENT OF A REAL-TIME SINGLE CHANNEL BRAIN–COMPUTER INTERFACE SYSTEM FOR DETECTION OF DROWSINESS

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237217500193 ◽

2017 ◽

Vol 29 (03) ◽

pp. 1750019 ◽

Cited By ~ 4

Author(s):

Malhar Pathak ◽

A. K. Jayanthy

Keyword(s):

Real Time ◽

Single Channel ◽

Detection System ◽

Brain Computer Interface ◽

Reference Electrode ◽

Computer Interface ◽

Processing Unit ◽

Eeg Signal ◽

Drowsiness Detection ◽

Interface System

Drowsiness or fatigue condition refers to feeling abnormally sleepy at an inappropriate time, especially during day time. It reduces the level of concentration and slowdown the response time, which eventually increases the error rate while doing any day-to-day activity. It can be dangerous for some people who require higher concentration level while doing their work. Study shows that 25–30% of road accidents occur due to drowsy driving. There are number of methods available for the detection of drowsiness out of which most of the methods provide an indirect measurement of drowsiness whereas electroencephalography provides the most reliable and direct measurement of the level of consciousness of the subject. The aim of this paper is to design and develop a portable and low cost brain–computer interface system for detection of drowsiness. In this study, we are using three dry electrodes out of which two active electrodes are placed on the forehead whereas the reference electrode is placed on the earlobe to acquire electroencephalogram (EEG) signal. Previous research shows that, there is a measurable change in the amplitude of theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave between the active state and the drowsy state and based on this fact theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave are separated from the normal EEG signal. The signal processing unit is interfaced with the microcontroller unit which is programmed to analyze the drowsiness based on the change in the amplitude of theta ([Formula: see text]) wave. An alarm will be activated once drowsiness is detected. The experiment was conducted on 20 subjects and EEG data were recorded to develop our drowsiness detection system. Experimental results have proved that our system has achieved real-time drowsiness detection with an accuracy of approximately 85%.

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Analysis of brain waves changes in stressful situations based on horror game with the implementation of virtual reality and brain-computer interface system: a case study

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2020-0050 ◽

2020 ◽

Vol 16 (4) ◽

Author(s):

Natalia Browarska ◽

Aleksandra Kawala-Sterniuk ◽

Przemysław Chechelski ◽

Jarosław Zygarlicki

Keyword(s):

Virtual Reality ◽

Brain Computer Interface ◽

Initial Study ◽

Computer Interface ◽

Expanded Group ◽

Brain Waves ◽

System A ◽

Eeg Data ◽

Interface System

AbstractObjectivesThis presents a case for fear and stress stimuli and afterward EEG data analysis.MethodsThe stress factor had been evoked by a computer horror game correlated with virtual reality (VR) and brain-computer interface (BCI) from OpenBCI, applied for the purpose of brain waves changes observation.ResultsResults obtained during the initial study were promising and provide conclusions for further research in this field carried out on an expanded group of involved participants.ConclusionsThe study provided very promising and interesting results. Further investigation with larger amount of participants will be carried out.

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A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

Data in Brief ◽

10.1016/j.dib.2021.106826 ◽

2021 ◽

Vol 35 ◽

pp. 106826

Author(s):

Giovanni Acampora ◽

Pasquale Trinchese ◽

Autilia Vitiello

Keyword(s):

Single Channel ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals

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An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

ACM Transactions on Management Information Systems ◽

10.1145/3458791 ◽

2021 ◽

Vol 12 (3) ◽

pp. 1-20

Author(s):

Damodar Reddy Edla ◽

Shubham Dodia ◽

Annushree Bablani ◽

Venkatanareshbabu Kuppili

Keyword(s):

Brain Computer Interface ◽

Wavelet Packet ◽

Stimulus Onset ◽

Computer Interface ◽

Band Pass Filter ◽

Eeg Signals ◽

Pass Filter ◽

Eeg Data ◽

Identification Test ◽

Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

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