scholarly journals A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

2022 ◽  
Author(s):  
Oana Andreea Rușanu
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Brain Computer Interface ◽  
Automatic Generation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Eye Blink ◽  
Eye Blinks ◽  
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.

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.

CLASSIFICATION OF EEG-BASED HAND GRASPING IMAGINATION USING AUTOREGRESSIVE AND NEURAL NETWORKS

2015 ◽  
Vol 78 (6-6) ◽  
Author(s):  
Esmeralda Contessa Djamal ◽  
Suprijanto Suprijanto ◽  
Steven J. Setiadi
Keyword(s):  
Neural Networks ◽  
Classification System ◽  
Brain Computer Interface ◽  
Small Error ◽  
Training Data ◽  
Ar Model ◽  
Computer Interface ◽  
Eeg Signal ◽  
The Mind

In the development of Brain Computer Interface (BCI), one important issue is the classification of hand grasping imagination. It is helpful for realtime control of the robotic or a game of the mind. BCI uses EEG signal to get information on the human. This research proposed methods to classify EEG signal against hand grasping imagination using Neural Networks.  EEG signal was recorded in ten seconds of four subjects each four times that were asked to imagine three classes of grasping (grasp, loose, and relax). Four subjects used as training data and four subjects as testing data. First, EEG signal was modeled in order 20 Autoregressive (AR) so that got AR coefficients being passed Neural Networks. The order of the AR model chosen based optimization gave a small error that is 1.96%. Then, it has developed a classification system using multilayer architecture and Adaptive Backpropagation as training algorithm. Using AR made training of the system more stable and reduced oscillation. Besides, the use of the AR model as a representation of the EEG signal improved the classification system accuracy of 68% to 82%. To verify the performance improvement of the proposed classification scheme, a comparison of the Adaptive Backpropagation and the conventional Backpropagation in training of the system. It resulted in an increase accuracy of 76% to 82%. The system was validated against all training data that produced an accuracy of 91%. The classification system that has been implemented in the software so that can be used as the brain computer interface.  

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.

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%.

scholarly journals A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5474 ◽  
Author(s):  
Dalin Yang ◽  
Trung-Hau Nguyen ◽  
Wan-Young Chung
Keyword(s):  
Steady State ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Home Automation ◽  
Eye Blink ◽  
Eye Blinks ◽  
Visually Evoked Potential ◽  
Automation Control ◽  
Bci System

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.

Sign in / Sign up

Export Citation Format

Share Document