TY - GEN
T1 - Automatic frequency bands segmentation using statistical similarity for power spectrum density based brain computer interfaces
AU - Lan, Tian
AU - Erdogmus, Deniz
AU - Pavel, Misha
AU - Mathan, Santosh
N1 - Funding Information:
Support by NASA for I. D. Boyd, Grant NCC2-582, and for T. Gokcen, Grant NCC2-420, is gratefully acknowledged.
PY - 2006
Y1 - 2006
N2 - Power spectrum density (PSD) of electroencephalogram (EEG) signals is a widely used feature for Brain Computer Interfaces (BCI). Usually, PSD features are integrated over different frequency bands, such as delta, theta, alpha, beta, gamma, which are based on well-established interpretations of EEG signals in prior experimental and clinical contexts. However, these predefined frequency bands do not necessarily relate to the optimal features for various BCI applications. In this paper, we propose an alternative feature dimensionality reduction method, which automatically determines the optimal number and the range of frequency bands. We applied the proposed method on EEG classification in the context of Augmented Cognition (AugCog) using BCI. The experimental results show that the proposed method can extract more robust features than features manually extracted from predefined frequency bands.
AB - Power spectrum density (PSD) of electroencephalogram (EEG) signals is a widely used feature for Brain Computer Interfaces (BCI). Usually, PSD features are integrated over different frequency bands, such as delta, theta, alpha, beta, gamma, which are based on well-established interpretations of EEG signals in prior experimental and clinical contexts. However, these predefined frequency bands do not necessarily relate to the optimal features for various BCI applications. In this paper, we propose an alternative feature dimensionality reduction method, which automatically determines the optimal number and the range of frequency bands. We applied the proposed method on EEG classification in the context of Augmented Cognition (AugCog) using BCI. The experimental results show that the proposed method can extract more robust features than features manually extracted from predefined frequency bands.
UR - http://www.scopus.com/inward/record.url?scp=40649114245&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:40649114245
SN - 0780394909
SN - 9780780394902
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 4650
EP - 4655
BT - International Joint Conference on Neural Networks 2006, IJCNN '06
T2 - International Joint Conference on Neural Networks 2006, IJCNN '06
Y2 - 16 July 2006 through 21 July 2006
ER -