Detecting mental states of alertness with genetic algorithm variable selection

The objective of the present work is to develop a method able to automatically determine mental states of vigilance; i.e., a person's state of alertness. Such a task is relevant to diverse domains, where a person is expected or required to be in a particular state. For instance, pilots or medical staffs are expected to be in a highly alert state, and this method could help to detect possible problems. In this paper, an approach is developed to predict the state of alertness (“normal” or “relaxed”) from the study of electroencephalographic signals (EEG) collected with a limited number of electrodes. The EEG of 58 participants in the two alertness states (116 records) were collected via a cap with 58 electrodes. After a data validation step, 19 subjects were retained for further analysis. A genetic algorithm was used to select an optimal subset of electrodes. Common spatial pattern (CSP) coupled to linear discriminant analysis (LDA) was used to build a decision rule and thus predict the alertness of the participants. Different subset sizes were investigated and the best result was obtained by considering 9 electrodes (correct classification rate of 73.68%).

[1]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[2]  P Naitoh,et al.  Modification of surface negative slow potential (CNV) in the human brain after total sleep loss. , 1971, Electroencephalography and clinical neurophysiology.

[3]  Osman Erogul,et al.  Automatic recognition of vigilance state by using a wavelet-based artificial neural network , 2005, Neural Computing & Applications.

[4]  Frédéric Alexandre,et al.  Alertness States Classification By SOM and LVQ Neural Networks , 2007 .

[5]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[6]  Hubert Cecotti,et al.  Convolutional Neural Network with embedded Fourier Transform for EEG classification , 2008, 2008 19th International Conference on Pattern Recognition.

[7]  Mervyn V. M. Yeo,et al.  Can SVM be used for automatic EEG detection of drowsiness during car driving , 2009 .

[8]  W. Rosenblith.  Some quantifiable aspects of the electrical activity of the nervous system (with emphasis upon responses to sensory stimuli). , 1959, Revue de laryngologie - otologie - rhinologie.

[9]  Charles W. Anderson,et al.  Classification of EEG Signals from Four Subjects During Five Mental Tasks , 2007 .

[10]  W. Walter,et al.  Contingent Negative Variation : An Electric Sign of Sensori-Motor Association and Expectancy in the Human Brain , 1964, Nature.

[11]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[12]  J J Tecce,et al.  A CNV rebound effect. , 1979, Electroencephalography and clinical neurophysiology.

[13]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[14]  M. Timsit-Berthier,et al.  Inversion de polarite de la variation contingente negative au cours d'etats d'endormissement* , 1981 .

[15]  Andrew C. N. Chen,et al.  Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. , 2002, Medical engineering & physics.

[16]  H. Jasper.  Report of the committee on methods of clinical examination in electroencephalography , 1958 .

[17]  Christa Neuper,et al.  Hidden Markov models for online classification of single trial EEG data , 2001, Pattern Recognit. Lett..