scholarly journals An efficient low-cost real-time brain computer interface system based on SSVEP

2010 ◽  
Vol 7 (5) ◽  
pp. 326-331 ◽  
Author(s):  
Ruen Shan Leow ◽  
Mahmoud Moghavvemi ◽  
Fatimah Ibrahim
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Interface System

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%.

Harnessing Brain Signals for Communication

ASHA Leader ◽  
2013 ◽  
Vol 18 (1) ◽  
Author(s):  
Yael Arbel
Keyword(s):  
Real Time ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Brain Signals ◽  
Interface System

The P300 Brain Computer Interface system converts people’s brain signals into words on a screen, enabling people who are completely paralyzed to communicate. The system records and analyzes brain activity in real time as the user focuses on conveying the intended message. The researchers behind this technology are working to develop a simplified version for efficient home use.

scholarly journals A Deep Learning Approach for Robotic Arm Control using Brain-Computer Interface

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Hand Movement ◽  
Substantial Improvement ◽  
Electrode System ◽  
Computer Interface ◽  
Robotic Arm ◽  
Robotic Hand

Brain-Computer Interface (BCI) is atechnology that enables a human to communicate with anexternal stratagem to achieve the desired result. This paperpresents a Motor Imagery (MI) – Electroencephalography(EEG) signal based robotic hand movements of lifting anddropping of an external robotic arm. The MI-EEG signalswere extracted using a 3-channel electrode system with theAD8232 amplifier. The electrodes were placed on threelocations, namely, C3, C4, and right mastoid. Signalprocessing methods namely, Butterworth filter and Sym-9Wavelet Packet Decomposition (WPD) were applied on theextracted EEG signals to de-noise the raw EEG signal.Statistical features like entropy, variance, standarddeviation, covariance, and spectral centroid were extractedfrom the de-noised signals. The statistical features werethen applied to train a Multi-Layer Perceptron (MLP) -Deep Neural Network (DNN) to classify the hand movementinto two classes; ‘No Hand Movement’ and ’HandMovement’. The resultant k-fold cross-validated accuracyachieved was 85.41% and other classification metrics, suchas precision, recall sensitivity, specificity, and F1 Score werealso calculated. The trained model was interfaced withArduino to move the robotic arm according to the classpredicted by the DNN model in a real-time environment.The proposed end to end low-cost deep learning frameworkprovides a substantial improvement in real-time BCI.

scholarly journals Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

2021 ◽  
Author(s):  
Oana Andreea Rușanu
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Automatic Generation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Eye Blink ◽  
Eye Blinks ◽  
Interface System ◽  
Temporal Sequences

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

A Rehabilitative Eye-Tracking Based Brain-Computer Interface for the Completely Locked-In Patient

2008 ◽  
pp. 1153-1160 ◽  
Author(s):  
Ziad O. Abu-Faraj ◽  
Habib C. Bou Sleiman ◽  
Waleed M. Al Katergi ◽  
Jean-Louis D. Heneine ◽  
Maya J. Mashaalany
Keyword(s):  
Eye Tracking ◽  
Mother Tongue ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Training Requirements ◽  
Processing Power ◽  
Interface System ◽  
Alternative Means ◽  
Ocular Motor Control

This article describes the design and development of a low-cost eye tracking-based brain-computer interface system for the rehabilitation of the completely locked-in patient having an intact ocular motor control to serve as an alternative means of communication (Abu-Faraj, Mashaalany, Bou Sleiman, Heneine, & Katergi, 2006). The developed system has been designed according to the following criteria: low cost, low processing power, simplicity of operation, little training requirements, minimal disturbance to the patient, and ease of customization to any mother tongue.

scholarly journals A feasibility study of a complete low-cost consumer-grade brain-computer interface system

Heliyon ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e03425 ◽  
Author(s):  
Victoria Peterson ◽  
Catalina Galván ◽  
Hugo Hernández ◽  
Ruben Spies
Keyword(s):  
Feasibility Study ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Computer Interface ◽  
Interface System
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