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A new approach for multiclass motor imagery recognition using pattern image features generated from common spatial patterns

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Abstract

This paper presents a new method that can classify multiple motor imageries and can be implemented in a realistic application because of its low computation time. The method proposes the use of pattern images, generated with the common spatial pattern (CSP) technique. The paper also suggests a new algorithm to determine the best frequency bands for optimal discrimination among the diverse motor imageries to classify. The pattern images and the state images, which represent the mental state of the user in a specific segment of time, are used to compute cross-correlation coefficients. Feature vectors, including characteristics obtained with CSP, and the mean and variance of the correlation coefficients were employed to design binary classifiers with support vector machines. In addition, the work includes a real-time simulation involving a sliding window technique. The proposed method was evaluated in four datasets: IVa, IVb and V from BCI Competition III and another provided by the software BCILAB, which compared with other state-of-the-art methods. The results that overcome surpassed the methods in these competitions and other state-of-the-art methods mentioned in this paper. The method also presents short computation time, robustness between subjects and capability to classify between multiple mental states.

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Acknowledgements

The authors greatly appreciate the support of Tecnologico Nacional de Mexico under Grants 5684.16-P and 6418.18-P to develop this work.

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  1. Tecnologico Naconal de Mexico/I.T. Chihuahua, Chihuahua, 31030, Mexico

    Mario I. Chacon-Murguia, Brenda E. Olivas-Padilla & Juan Ramirez-Quintana

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  1. Mario I. Chacon-Murguia
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  2. Brenda E. Olivas-Padilla
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  3. Juan Ramirez-Quintana
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Correspondence to Mario I. Chacon-Murguia.

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Chacon-Murguia, M.I., Olivas-Padilla, B.E. & Ramirez-Quintana, J. A new approach for multiclass motor imagery recognition using pattern image features generated from common spatial patterns. SIViP 14, 915–923 (2020). https://doi.org/10.1007/s11760-019-01623-0

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