Development of an Effective Computing Framework for Classification of Motor Imagery EEG Signals for Brain–Computer Interface

2020 ◽  
pp. 17-35
Author(s):  
Pinisetty Sri Ramya ◽  
Kondabolu Yashasvi ◽  
Arshiya Anjum ◽  
Abhijit Bhattacharyya ◽  
Ram Bilas Pachori
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Computing Framework

Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications

2019 ◽  
pp. 241-251
Author(s):  
Subrota Mazumdar ◽  
Rohit Chaudhary ◽  
Suruchi Suruchi ◽  
Suman Mohanty ◽  
Divya Kumari ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Nearest Neighbor ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Test Cases ◽  
Multiple Channels ◽  
Eeg Signals ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.

Testing the Self-Similarity Exponent to Feature Extraction in Motor Imagery Based Brain Computer Interface Systems

2015 ◽  
Vol 25 (14) ◽  
pp. 1540023
Author(s):  
Germán Rodríguez-Bermúdez ◽  
Miguel Ángel Sánchez-Granero ◽  
Pedro J. García-Laencina ◽  
Manuel Fernández-Martínez ◽  
José Serna ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Information Acquisition ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Self Similarity ◽  
Self Similar ◽  
Bci System

A Brain Computer Interface (BCI) system is a tool not requiring any muscle action to transmit information. Acquisition, preprocessing, feature extraction (FE), and classification of electroencephalograph (EEG) signals constitute the main steps of a motor imagery BCI. Among them, FE becomes crucial for BCI, since the underlying EEG knowledge must be properly extracted into a feature vector. Linear approaches have been widely applied to FE in BCI, whereas nonlinear tools are not so common in literature. Thus, the main goal of this paper is to check whether some Hurst exponent and fractal dimension based estimators become valid indicators to FE in motor imagery BCI. The final results obtained were not optimal as expected, which may be due to the fact that the nature of the analyzed EEG signals in these motor imagery tasks were not self-similar enough.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information

scholarly journals Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

2019 ◽  
Vol 3 (3) ◽  
pp. 80
Author(s):  
Muhammad Fawaz Saputra ◽  
Noor Akhmad Setiawan ◽  
Igi Ardiyanto
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Short Term Memory ◽  
Brain Computer Interface ◽  
Correction Method ◽  
Computer Interface ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Learning Methods ◽  
Motor Movements

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM.

Sign in / Sign up

Export Citation Format

Share Document