Brain wave classification using long short - term memory based OPTICAL predictor

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL

Emprego de Banco de Filtros e do Teorema de Imersão de Takens em Padrões Espaciais para a Classificação de Imagética Motora em Interfaces Cérebro-Computador

As Interfaces Cérebro-Computador (BCI) são sistemas que provêm uma alternativa para que pessoas com perda severa ou total do controle motor possam inte- ragir com o ambiente externo. Para mapear intenções individuais em operações de má- quina, os sistemas de BCI empregam um conjunto de etapas que envolvem a captura e pré-processamento dos sinais cerebrais, a extração e seleção de suas características mais relevantes e a classificação das intenções. Neste trabalho, diferentes abordagens para a extração de características de sinais cerebrais foram avaliadas, a mencionar: i) Padrões Espectro-Espaciais Comuns (CSSP); ii) Padrões Esparsos Espectro-Espaciais Comuns (CSSSP); iii) CSSP com banco de filtros (FBCSSP); e, finalmente, iv) CSSSP com banco de filtros (FBCSSSP). Em comum, essas técnicas utilizam de filtragem de banda de frequências e reconstrução de espaços para ressaltar similaridades entre si- nais. A técnica de Seleção de Características baseada em Informação Mútua (MIFS) foi adotada para a redução de dimensionalidade das características extraídas e, em se- guida, Máquinas de Vetores de Suporte (SVM) foram utilizadas para a classificação do espaço de exemplos. Os experimentos consideraram o conjunto de dados BCI Compe- tition IV-2b, o qual conta com sinais produzidos pelos eletrodos nas posições C3, Cz e C4 a fim de identificar as intenções de movimentação das mãos direita e esquerda. Conclui-se, a partir dos índices kappa obtidos, que os extratores de características adotados podem apresentar resultados equiparáveis ao estado da arte.

Extraction subject-specific motor imagery time-frequency patterns for single trial EEG classification

PubMedID: 17010962We introduce a new adaptive time-frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain-computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time-frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain-computer interfaces. © 2006 Elsevier Ltd. All rights reserved.National Council for Scientific ResearchThis project was supported by The National Scientific Research Council of Turkey (TUBITAK). Nuri F. Ince received his Ph.D. degree in Electrical and Electronics Engineering from Cukurova University, Adana, Turkey in 2005. During his Ph.D. he was supported by the National Scientific Research Council of Turkey with International Joint Ph.D. scholarship. Currently he is a Post-Doc Associate at the University of Minnesota. His research interest include neural engineering, wearable medical sensors, adaptive time frequency analysis and classification of biomedical signals. Ahmed H. Tewfik received his B.Sc. degree from Cairo University, Cairo Egypt, in 1982 and his M.Sc., E.E. and Sc.D. degrees from the Massachusetts Institute of Technology, Cambridge, MA, in 1984, 1985 and 1987 respectively. Dr. Tewfik has worked at Alphatech, Inc., Burlington, MA in 1987. He is the E. F. Johnson professor of Electronic Communications with the department of Electrical Engineering at the University of Minnesota. He served as a consultant to MTS Systems, Inc., Eden Prairie, MN and Rosemount, Inc., Eden Prairie, MN and worked with Texas Instruments and Computing Devices International. From August 1997 to August 2001, he was the President and CEO of Cognicity, Inc., an entertainment marketing software tools publisher that he co-founded, on partial leave of absence from the University of Minnesota. Prof. Tewfik is a Fellow of the IEEE. He was a Distinguished Lecturer of the IEEE Signal Processing Society in 1997–1999. He received the IEEE third Millennium award in 2000. His research interest include Genomics and bioinformatics; Brain Computer Interfaces, Analysis and Classification of EEG/MEG with adaptive time frequency bases, Wearable Medical Sensors; food inspection; programmable wireless networks; sparse signal representations and data centric computing. In the past, He made seminal contributions to low power multimedia communications, adaptive search and data acquisition strategies for world wide web applications, radar and dental/medical imaging, monitoring of machinery via acoustic emissions, industrial measurements, wavelet signal processing and fractals. Sami Arica received his Ph.D. degree in Electrical and Electronics Engineering from Cukurova University, Adana, Turkey in 1999. Currently he is a Assistant Professor at the Department of Electrical and Electronics Engineering of Cukurova University. His research interest include signal / image processing, filter banks and wavelets