Autors: Neshov, N. N., Manolova, A. H., Draganov, I. R., Tonchev, K.T., Boumbarov, O.L.
Title: Classification of Mental Tasks from EEG Signals using Spectral Analysis, PCA and SVM
Keywords: Electroencephalography (EEG); Brain Computer Interface (BCI); Fast Fourier Transform (FFT); Principal component analysis (PCA); Support vector machine (SVM)

Abstract: Signals provided by the Electroencephalography (EEG) are widely used in brain-computer interface (BCI) applications. They can be further analyzed and used for thinking activity recognition. In this paper we proposed an algorithm that is able to recognize five mental tasks using 6 channel EEG data. The main idea is to separate the raw EEG signals into several frames and compute their spectrums. Next, a second-order derivative of Gaussian is applied to extract features and an optimum Gaussian kernel parameters grid search is performed with the help of cross-validation. The extracted features are further reduced by Principal Component Analysis. The processed data is utilized to train SVM classifier which is used for mental tasks recognition afterwards. The performance of the algorithm is estimated on publically available dataset. In terms of 5 folds cross-validation we obtained an average of 82.7% recognition rate (accuracy).

References

    Issue

    Cybernetics and Information Technologies, vol. 18, issue 1, pp. 81-92, 2018, Bulgaria, Bulgarian Academy of Sciences, DOI 10.2478/cait-2018-0007

    Copyright Sciendo

    Цитирания (Citation/s):
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    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science