scholarly journals A Novel Design of 4-Class BCI Using Two Binary Classifiers and Parallel Mental Tasks

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Tao Geng ◽  
John Q. Gan ◽  
Matthew Dyson ◽  
Chun SL Tsui ◽  
Francisco Sepulveda
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Classification Algorithms ◽  
Single Trial ◽  
Linear Discriminant ◽  
The Subject ◽  
Offline Analysis ◽  
Binary Classifiers ◽  
Novel Design ◽  
Mental Tasks

A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.

Channel Selection and Feature Extraction of ECoG-Based Brain-Computer Interface Using Band Power

2010 ◽  
Vol 44-47 ◽  
pp. 3564-3568 ◽  
Author(s):  
Hai Bin Zhao ◽  
Chong Liu ◽  
Chun Yang Yu ◽  
Hong Wang
Keyword(s):  
Feature Extraction ◽  
Brain Computer Interface ◽  
Channel Selection ◽  
Computer Interface ◽  
Fisher Linear Discriminant ◽  
Data Set ◽  
Linear Discriminant ◽  
Distinctive Features ◽  
The Subject ◽  
Components Analysis

Electrocorticography (ECoG) signals have been proved to be associated with different types of motor imagery and have used in brain-computer interface (BCI) research. This paper studies the channel selection and feature extraction using band powers (BP) for a typical ECoG-based BCI system. The subject images movement of left finger or tongue. Firstly, BP features were used for channel selection, and 11 channels which had distinctive features were selected from 64 channels. Then, the features of ECoG signals were extracted using BP, and the dimension of feature vector was reduced with principal components analysis (PCA). Finally, Fisher linear discriminant analysis (LDA) was used for classification. The results of the experiment showed that this algorithm has got good classification accuracy for the test data set.

scholarly journals Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Robert Leeb ◽  
Doron Friedman ◽  
Gernot R. Müller-Putz ◽  
Reinhold Scherer ◽  
Mel Slater ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Beta Oscillations ◽  
Brain Waves ◽  
The Subject ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
Spinal Cord Injured

The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to “go” from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.

Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface

2021 ◽  
Author(s):  
ShuRui Li ◽  
Jing Jin ◽  
Ian Daly ◽  
Chang Liu ◽  
Andrzej Cichocki
Keyword(s):  
Feature Selection ◽  
Brain Computer Interface ◽  
Feature Selection Method ◽  
Event Related Potentials ◽  
Selection Method ◽  
Computer Interface ◽  
Feature Subset ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Menger Curvature

Abstract Brain–computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.

EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

2011 ◽  
Vol 3 (3) ◽  
pp. 43-56 ◽  
Author(s):  
Ling Zou ◽  
Xinguang Wang ◽  
Guodong Shi ◽  
Zhenghua Ma
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Average Power ◽  
Computer Interface ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Reconstructed Signal

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

scholarly journals Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

2019 ◽  
Vol 252 ◽  
pp. 03010
Author(s):  
Małgorzata Plechawska-Wójcik ◽  
Monika Kaczorowska ◽  
Bernadetta Michalik
Keyword(s):  
Comparative Analysis ◽  
Supervised Classification ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Process Comparison ◽  
Eeg Data ◽  
Comparative Accuracy ◽  
Supervised Classification Methods

The main goal of the paper is to perform a comparative accuracy analysis of the two-group classification of EEG data collected during the P300-based brain-computer interface tests. The brain-computer interface is a technology that allows establishing communication between a human brain and external devices. BCIs may be applied in medicine to improve the life of disabled people and as well for entertainment. The P300 is an event-related potential (ERP) appearing about 300 ms after the occurrence of the stimulus of visual, auditory or sensory nature. It is based on the phenomenon observed in anticipation for a target event among non-target events. The 21-channel 201 Mitsar amplifier was used during the experiment to store EEG data from seven electrodes placed on the dedicated cap. The study was conducted on a group of five persons using P300 scenario available in OpenVibe software. The experiment was based on three steps the classifier learning process, comparison and averaging of the obtained result and the final test of the classifier. The comparative analysis was performed with the application of two supervised classification methods: Linear Discriminant Analysis (LDA) and Multi-layer Perceptron (MLP). The preliminary data analysis, extraction and feature selection was performed prior to the classification.

Sign in / Sign up

Export Citation Format

Share Document