<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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). Additional experiments were conducted using leave-one-out cross-validation where 67.2% correct classification was reported. Comparison to several state-of-the art methods reveals the advantages of the proposed algorithm.</p></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). Additional experiments were conducted using leave-one-out cross-validation where 67.2% correct classification was reported. Comparison to several state-of-the art methods reveals the advantages of the proposed algorithm.

Classification of Mental Tasks from EEG Signals Using Spectral Analysis, PCA and SVM

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Signals provided by the ElectroEncephaloGraphy (EEG) are widely used in Brain-Computer Interface (BCI) applications. They can be further analyzed and used for...

Classification of Mental Tasks from EEG Signals Using Spectral Analysis, PCA and SVM

Pubblicato online: 30 mar 2018

Pagine: 81 - 92

Ricevuto: 28 nov 2017

Accettato: 20 dic 2017

DOI: https://doi.org/10.2478/cait-2018-0007

Parole chiave
ElectroEncephaloGraphy (EEG), Brain Computer Interface (BCI), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), Support Vector Machine (SVM)

© 2018 Nikolay N. Neshov et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Classification of Mental Tasks from EEG Signals Using Spectral Analysis, PCA and SVM

Pubblicato online: 30 mar 2018

Pagine: 81 - 92

Ricevuto: 28 nov 2017

Accettato: 20 dic 2017

DOI: https://doi.org/10.2478/cait-2018-0007

Parole chiaveElectroEncephaloGraphy (EEG), Brain Computer Interface (BCI), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), Support Vector Machine (SVM)

© 2018 Nikolay N. Neshov et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
ElectroEncephaloGraphy (EEG), Brain Computer Interface (BCI), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), Support Vector Machine (SVM)