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

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.

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An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface

2015 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2015.7280754 ◽

2015 ◽

Cited By ~ 17

Author(s):

Pramod Gaur ◽

Ram Bilas Pachori ◽

Hui Wang ◽

Girijesh Prasad

Keyword(s):

Empirical Mode Decomposition ◽

Motor Imagery ◽

Brain Computer Interface ◽

Computer Interface ◽

Eeg Signals ◽

Filtering Method ◽

Mode Decomposition

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

Advances in Computational Intelligence Techniques - Algorithms for Intelligent Systems ◽

10.1007/978-981-15-2620-6_2 ◽

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

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Investigation of EEG Signal Classification Techniques for Brain Computer Interface

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5679.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3266-3274

Keyword(s):

Feature Extraction ◽

Classification Accuracy ◽

Brain Computer Interface ◽

Signal Classification ◽

Computer Interface ◽

Support Vector ◽

Eeg Signals ◽

Classification Techniques ◽

Feature Extraction And Selection ◽

Bci System

Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

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

Early Detection of Neurological Disorders Using Machine Learning Systems - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-8567-1.ch013 ◽

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.

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