Journal ArticleDOI
Subject and class specific frequency bands selection for multiclass motor imagery classification
Heung-Il Suk,Seong-Whan Lee +1 more
TLDR
A novel method of selecting subject and class specific frequency bands based on the analysis of a channel‐frequency matrix, which is applicable to other kinds of single‐trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery‐based BCI applications.Abstract:
EEG-based discrimination among motor imagery states has been widely studied for brain-computer interfaces (BCIs) due to the great potential for real-life applications. However, in terms of designing a motor imagery-based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel method of selecting subject and class specific frequency bands based on the analysis of a channel-frequency matrix, which we call a channel-frequency map. We operate the classification process for each frequency band individually, i.e., spatial filtering, feature extraction, and classification, and determine a class label for an input EEG by considering the outputs from multiple classifiers together at the end. From our experiments on a public dataset of BCI Competition IV (2008) II-a that includes four motor imagery tasks from nine subjects, the proposed algorithm outperformed the common spatial pattern (CSP) algorithm in a broad band and a filter bank CSP algorithm on average in terms of cross-validation and session-to-session transfer rate. Furthermore, a considerable increase of classification accuracy has been achieved for certain subjects. We also would like to note that the proposed data-driven frequency bands selection method is applicable to other kinds of single-trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery-based BCI applications. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 123–130, 2011
(WCU (World Class University) Program (R31-10008-0) through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology.)read more
Citations
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Journal ArticleDOI
A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces
Heung-Il Suk,Seong-Whan Lee +1 more
TL;DR: A novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches is proposed.
Journal ArticleDOI
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces
TL;DR: This paper proposes to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size, and surveys existing approaches to reduce or suppress calibration time and proposes three different methods to do so.
Journal ArticleDOI
Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals
TL;DR: The feasibility of intuitive robotic arm control based on EEG signals for real-world environments is demonstrated and a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework is proposed.
Journal ArticleDOI
A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification
TL;DR: A novel MI classification framework is first introduced, including a new 3D representation of EEG, a multi-branch 3D convolutional neural network (3D CNN) and the corresponding classification strategy, which reaches state-of-the-art classification kappa value level and significantly outperforms other algorithms.
Journal ArticleDOI
Channel selection and classification of electroencephalogram signals
Jianhua Yang,Harsimrat Singh,Evor L. Hines,Friederike Schlaghecken,Daciana Iliescu,Mark S. Leeson,Nigel G. Stocks +6 more
TL;DR: It is demonstrated that GNMM is able to perform effective channel selections/reductions, which not only reduces the difficulty of data collection, but also greatly improves the generalization of the classifier.
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