A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

The paper presents an investigation into a time-frequency (TF) method for extracting features from the electroencephalogram (EEG) recorded from subjects performing imagination of left- and right-hand movements. The feature extraction procedure (FEP) extracts frequency domain information to form features whilst time-frequency resolution is attained by localising the fast Fourier transformations (FFTs) of the signals to specific windows localised in time. All features are extracted at the rate of the signal sampling interval from a main feature extraction (FE) window through which all data passes. Subject-specific frequency bands are selected for optimal feature extraction and intraclass variations are reduced by smoothing the spectra for each signal by an interpolation (IP) process. The TF features are classified using linear discriminant analysis (LDA). The FE window has potential advantages for the FEP to be applied in an online brain-computer interface (BCI). The approach achieves good performance when quantified by classification accuracy (CA) rate, information transfer (IT) rate, and mutual information (MI). The information that these performance measures provide about a BCI system is analysed and the importance of this is demonstrated through the results.

[1]  Bruce J. Fisch,et al.  Fisch and Spehlmann's Eeg Primer: Basic Principles of Digital and Analog Eeg , 1999 .

[2]  Masud Mansuripur,et al.  Introduction to information theory , 1986 .

[3]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[4]  K.-R. Muller,et al.  Linear and nonlinear methods for brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  G Pfurtscheller,et al.  EEG-based communication: improved accuracy by response verification. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  G Pfurtscheller,et al.  Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. , 1998, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  A. Schlogl,et al.  Information transfer of an EEG-based brain computer interface , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[8]  Stephen J. Elliott,et al.  Efficient estimation of a time-varying dimension parameter and its application to EEG analysis , 2003, IEEE Transactions on Biomedical Engineering.

[9]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[10]  G Pfurtscheller,et al.  Estimating the Mutual Information of an EEG-based Brain-Computer Interface , 2002, Biomedizinische Technik. Biomedical engineering.

[11]  A. Emery.  Population frequencies of inherited neuromuscular diseases—A world survey , 1991, Neuromuscular Disorders.

[12]  A Kostov,et al.  Parallel man-machine training in development of EEG-based cursor control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[13]  M. Stokes,et al.  Probabilistic methods in BCI research , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Boualem Boashash,et al.  A comparative study of spectral estimation techniques for noisy non-stationary signals with application to EEG data , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[15]  G Pfurtscheller,et al.  Frequency component selection for an EEG-based brain to computer interface. , 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[16]  G Pfurtscheller,et al.  Using time-dependent neural networks for EEG classification. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[17]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[18]  Girijesh Prasad,et al.  EEG-Based Communication: A Time Series Prediction Approach , 2003 .

[19]  Gert Pfurtscheller,et al.  Distinction Sensitive Learning Vector Quantization (DSLVQ) application as a classifier based feature selection method for a Brain Computer Interface , 1995 .

[20]  Fazlollah M. Reza,et al.  Introduction to Information Theory , 2004, Lecture Notes in Electrical Engineering.

[21]  G Pfurtscheller,et al.  Adaptive Autoregressive Modeling used for Single-trial EEG Classification - Verwendung eines Adaptiven Autoregressiven Modells für die Klassifikation von Einzeltrial-EEG-Daten , 1997, Biomedizinische Technik. Biomedical engineering.

[22]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[23]  William Z Rymer,et al.  Brain-computer interface technology: a review of the Second International Meeting. , 2003, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[24]  Wenwei Yu,et al.  On‐line learning method for EMG prosthetic hand control , 2001 .

[25]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  G. R. Muller,et al.  Brain oscillations control hand orthosis in a tetraplegic , 2000, Neuroscience Letters.

[27]  Karen A. Moxon.  Brain-control interfaces for sensory and motor prosthetic devices , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).