TY - GEN
T1 - Estimating cognitive state using EEG signals
AU - Lan, Tian
AU - Adami, Andre
AU - Erdogmus, Deniz
AU - Pavel, Misha
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2005
Y1 - 2005
N2 - Using EEG signals to estimate cognitive state has drawn increasing attention in recently years, especially in the context of brain-computer interface (BCI) design. However, this goal is extremely difficult because, in addition to the complex relationships between the cognitive state and EEG signals that yields the non-stationarity of the features extracted from EEG signals, there are artefacts introduced by eye blinks and head and body motion. In this paper, we present a classification system, which can estimate the subject's cognitive state from the measured EEG signals. In the proposed system, a mutual information based method is employed to reduce the dimensionality of the features as well as to increase the robustness of the system. A committee of three classifiers was implemented and the majority voting results of the committee are taken to be the final decisions. The results of a preliminary test with data from freely moving subjects performing various tasks as opposed to the strictly controlled experimental set-ups of BCI provide strong support for this approach.
AB - Using EEG signals to estimate cognitive state has drawn increasing attention in recently years, especially in the context of brain-computer interface (BCI) design. However, this goal is extremely difficult because, in addition to the complex relationships between the cognitive state and EEG signals that yields the non-stationarity of the features extracted from EEG signals, there are artefacts introduced by eye blinks and head and body motion. In this paper, we present a classification system, which can estimate the subject's cognitive state from the measured EEG signals. In the proposed system, a mutual information based method is employed to reduce the dimensionality of the features as well as to increase the robustness of the system. A committee of three classifiers was implemented and the majority voting results of the committee are taken to be the final decisions. The results of a preliminary test with data from freely moving subjects performing various tasks as opposed to the strictly controlled experimental set-ups of BCI provide strong support for this approach.
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M3 - Conference contribution
AN - SCOPUS:84863691855
SN - 1604238216
SN - 9781604238211
T3 - 13th European Signal Processing Conference, EUSIPCO 2005
SP - 1387
EP - 1390
BT - 13th European Signal Processing Conference, EUSIPCO 2005
T2 - 13th European Signal Processing Conference, EUSIPCO 2005
Y2 - 4 September 2005 through 8 September 2005
ER -