Automatic recognition of alertness and drowsiness from EEG by an artificial neural network.

We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum EEG recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum EEG as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. The experimental data were collected from 17 subjects. Two experts in EEG interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization (LVQ) neural network. We showed that the LVQ neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of LVQ were validated using the data recorded in 12 healthy volunteer subjects, yet whose EEG recordings have not been used for the training of the ANN. The statistics were used as a measure of potential applicability of the LVQ: the t-distribution showed that matching between the human assessment and the network output was 94.37+/-1.95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training.

[1]  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.

[2]  Tzyy-Ping Jung,et al.  Estimating level of alertness from EEG , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  A. Gevins,et al.  Detecting transient cognitive impairment with EEG pattern recognition methods. , 1999, Aviation, space, and environmental medicine.

[4]  Gert Pfurtscheller,et al.  Automatic differentiation of multichannel EEG signals , 2001, IEEE Transactions on Biomedical Engineering.

[5]  Georg Dorffner,et al.  Using Selforganizing Feature Maps to Classify EEG Coherence Maps , 1993 .

[6]  G. Pfurtscheller,et al.  Prediction of the side of hand movements from single-trial multi-channel EEG data using neural networks. , 1992, Electroencephalography and clinical neurophysiology.

[7]  G. Pfurtscheller,et al.  On-line EEG classification during externally-paced hand movements using a neural network-based classifier. , 1996, Electroencephalography and clinical neurophysiology.

[8]  Ivan Marsic,et al.  A system for medical consultation and education using multimodal human/machine communication , 1998, IEEE Transactions on Information Technology in Biomedicine.

[9]  G Pfurtscheller,et al.  Sleep Classification in Infants Based on Artificial Neural Networks. Schlafklassifikation mit Hilfe neuronaler Netzwerke , 1992, Biomedizinische Technik. Biomedical engineering.

[10]  T. Sejnowski,et al.  Estimating alertness from the EEG power spectrum , 1997, IEEE Transactions on Biomedical Engineering.

[11]  T. Jung,et al.  Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness. , 1996, Brain research. Cognitive brain research.

[12]  E. Clothiaux,et al.  Neural Networks and Their Applications , 1994 .

[13]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[14]  B. J. Wilson,et al.  Alertness monitor using neural networks for EEG analysis , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).

[15]  J.C. Principe,et al.  Sleep staging automaton based on the theory of evidence , 1989, IEEE Transactions on Biomedical Engineering.

[16]  P. Escourrou,et al.  Needs and costs of sleep monitoring. , 2000, Studies in health technology and informatics.

[17]  O. Ozdamar,et al.  Wavelet preprocessing for automated neural network detection of EEG spikes , 1995 .

[18]  G Pfurtscheller,et al.  Selection of electrode positions for an EEG-based Brain Computer Interface (BCI). Auswahl von Elektrodenpositionen für ein auf EEG-Ableitungen basierendes Brain Computer Interface (BCI) , 1994, Biomedizinische Technik. Biomedical engineering.

[19]  G Pfurtscheller,et al.  EEG Classification by Learning Vector Quantization - EEG-Klassifikation mit Hilfe eines Learning Vector Quantizers , 1992, Biomedizinische Technik. Biomedical engineering.

[20]  J. Santamaria,et al.  The EEG of Drowsiness in Normal Adults , 1987, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[21]  P. Nunez,et al.  Neocortical Dynamics and Human EEG Rhythms , 1995 .

[22]  B.H. Jansen,et al.  Knowledge-based approach to sleep EEG analysis-a feasibility study , 1989, IEEE Transactions on Biomedical Engineering.

[23]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[24]  M M Mitler,et al.  A normative study of the maintenance of wakefulness test (MWT). , 1997, Electroencephalography and clinical neurophysiology.

[25]  David Mackay,et al.  Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .

[26]  Oscal T.-C. Chen,et al.  EEG pattern recognition-arousal states detection and classification , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[27]  Tzyy-Ping Jung,et al.  Using Feedforward Neural Networks to Monitor Alertness from Changes in EEG Correlation and Coherence , 1995, NIPS.

[28]  P. Achermann,et al.  A new method for detecting state changes in the EEG: exploratory application to sleep data , 1998, Journal of sleep research.

[29]  J J Chen,et al.  Application of dipole modeling in localization of mesio-temporal epileptogenic focus. , 1997, Proceedings of the National Science Council, Republic of China. Part B, Life sciences.

[30]  N Pradhan,et al.  Detection of seizure activity in EEG by an artificial neural network: a preliminary study. , 1996, Computers and biomedical research, an international journal.

[31]  Y. Wada,et al.  Inter- and Intrahemispheric EEG Coherence during Light Drowsiness , 1996, Clinical EEG.