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      EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach

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          Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources.

          An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
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            Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture

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              Estimating alertness from the EEG power spectrum.

              In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.
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                Author and article information

                Journal
                EURASIP Journal on Advances in Signal Processing
                EURASIP J. Adv. Signal Process.
                Hindawi Limited
                1687-6180
                December 2008
                November 4 2008
                December 2008
                : 2008
                : 1
                Article
                10.1155/2008/519480
                159e55ab-0c15-4af1-8919-ef36a76825df
                © 2008
                History

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