Classification of Driver’s Cognitive Responses From EEG Analysis

Liang, Sheng-Fu; Lin, Chin‐Teng; Wu, Ruei-Cheng; Huang, Teng‐Yi; Chao, Wen-Hung

doi:10.1109/iscas.2005.1464548

Cited by 3 publications

(2 citation statements)

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“…The latter agrees with Thorpe, Fize, and Marlot (1996) who suggested that the recognition of familiar objects generally occurs within 150 ms after stimulus onset. Another local maximum in ACA that is evident for all assessed participants at the approximate latency of 300 ms, perhaps, can be attributed to the P300 component of VEP that was previously reported as contributing to the classification of traffic light color (Bayliss & Ballard, 2000;Liang et al, 2005;Lin et al, 2007).…”

Section: Discussionmentioning

confidence: 53%

“…Lin et al (2007) have further developed the yellow or red classification, reporting similar accuracy of 85%. The authors also extended their study by including all three traffic light signals (Liang, Lin, Wu, Huang, & Chao, 2005;Lin, et al, 2008). However, since the participants were instructed to act based on the traffic light color they perceive, we may hypothesize that the cognitive response to the driving environment may be contaminated by the action-related cortical response.…”

Section: Evaluating Drivers' Response To Traffic Lights: Literature Rmentioning

confidence: 99%

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Spatial Vector-based Approach to the ERP Analysis as Applied to an EEG-based Discrimination of Traffic Light Signals

Sultana

Tcheslavski

2019

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The purpose of the study was to assess the utility of the spatial vector-based representation of multichannel electroencephalography (EEG; when each spatial vector denotes an "instantaneous" sample of cortical activation evolving over time) in the analysis of cortical responses to visual stimulation-as opposed to the traditional, temporal vector-based approach, when vectors are associated with distinct EEG channels. This representation was used in the analysis of EEG collected in the virtual traffic light environment with the attempt to determine the color of traffic light perceived by four participants. Kruskal-Wallis (K-W) analysis of variance was implemented for selected EEG electrodes. To utilize all available information, discrimination value was evaluated next for 32dimensional EEG spatial vectors followed by modified "k nearest neighbors" (knn) classification. K-W test indicated that EEG samples at selected electrodes are different between different colors of traffic light and when observed for specific latencies. The average accuracy of a modified three-class knn classifier was approaching 60% (the random assignment would yield approximately 33%) for the specific poststimuli latencies. The proposed technique allows analyzing stimulation-synchronized cortical activity with the temporal resolution generally determined by the sampling rate of the neuroimaging modality. The discrimination value appears instrumental for predicting the clusterability of data assessed. Stimulation-evoked cortical responses are often of interest in studies of human cognition. The proposed technique may overcome the low signal-to-noise limitation of the traditional evoked response potential (ERP) analysis and possibly provide means to assess such responses under the real-time constraint.

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