scholarly journals Design of Single Channel Portable EEG Signal Acquisition System for Brain Computer Interface Application

2016 ◽  
Vol 3 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Amlan Jyoti Bhagawati ◽  
Riku Chutia
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Acquisition System ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Eeg Signal

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

2017 ◽  
Vol 29 (03) ◽  
pp. 1750019 ◽  
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%.

Development of single channel EEG Acquisition system for BCI applications

2021 ◽  
pp. 4705-4709
Author(s):  
V.G. Rajendran ◽  
Jayalalitha S. ◽  
Adalarasu K. ◽  
Nirmalraj T.
Keyword(s):  
Integrated Circuits ◽  
Bandpass Filter ◽  
Single Channel ◽  
Acquisition System ◽  
Computer Interface ◽  
Brain Machine Interface ◽  
Instrumentation Amplifier ◽  
Signal Acquisition ◽  
Level Shifter ◽  
Machine Interface

Brain-Computer Interface (BCI) plays a major role in current technologies such as rehabilitation, control of devices, and various medical applications. BCI or brain-machine interface provides direct communication between a brain signal and an external device. In this paperwork, a detailed survey was carried out with the design of single-channel EEG system for various applications. Also, this paper mainly focused on the development of single-channel electroencephalography (EEG) signal acquisition system which includes a preamplifier, bandpass filter, post-amplifier and level shifter circuits. The design of the preamplifier and post-amplifier circuit was carried out by integrated circuits (IC) such as instrumentation amplifier IN128P and bandpass filter with the help of low power operational amplifier LM324. The developed single-channel acquisition board was tested by acquiring an electrooculogram (EOG) signal with closed and opened eye conditions. The acquired signal is displayed and stored in the computer with the help of the HBM-DAQ unit.

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

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106826
Author(s):  
Giovanni Acampora ◽  
Pasquale Trinchese ◽  
Autilia Vitiello
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

scholarly journals Design of a general brain-computer interface

2011 ◽  
Vol 22 (6) ◽  
pp. 638-646 ◽  
Author(s):  
Alessandro B. Benevides ◽  
Mário Sarcinelli-Filho ◽  
Teodiano F. Bastos Filho
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Eeg Signal ◽  
Time Process ◽  
Class Separation ◽  
Power Spectral ◽  
Mental Tasks

This paper presents the classification of three mental tasks, using the EEG signal and simulating a real-time process, what is known as pseudo-online technique. The Bayesian classifier is used to recognize the mental tasks, the feature extraction uses the Power Spectral Density, and the Sammon map is used to visualize the class separation. The choice of the EEG channel and sampling frequency is based on the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifications.

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