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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dahal, N. Nandagopal, N. Nafalski, A. Nedic, Z. |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Electrical and Information Engineering, UniSA Adelaide, SA, AUSTRALIA (Nafalski, A.; Nedic, Z.) || School of Electrical and Information Engineering, University of South Australia, Adelaide, AUSTRALIA (Dahal, N.) || Defence Science and Technology Organization (DSTO), University of South Australia, Adelaide, AUSTRALIA (Nandagopal, N.) |
| Abstract | Understanding the secrets underlying the brain functioning would be the noble achievement of this era. Learning how brain learns would be the milestone to guide the researchers of artificial intelligence, neurology and psychology. With the advent of “Integrate and Fire” model of neuron proposed about a hundred years ago, the brain research has picked up its pace in the study of different aspects of brain functionality. Many cognitive architectures have been proposed with an aim of simulating and understanding human cognition. On the other hand, many technologies have emerged that can measure the parameters of the brain activity. Among them, Electroencephalogram (EEG) stands as a reliable tool in the study of brain functioning. Simplified wireless EEGs are readily available now which can send data recorded by its electrodes to a computer for further processing. We have chosen this tool to detect different aspects of cognition and to predict the brain functioning behind it. A lot of studies from the past two decades have already revealed varying EEG patterns related to cognition. In this paper, we have proposed to extract different features from visual, tactile, auditory and psychomotor stimuli to work on different cognitive aspects such as memory, emotion, arousal, fatigue and distraction and to investigate its affect on the EEG. A methodology to model cognitive functions by relating the varying event related potential, brain waves, spectral density and latency in EEG outcomes are then related with the stimuli features to predict the cognitive state of mind. |
| Starting Page | 1045 |
| Ending Page | 1049 |
| File Size | 516450 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781457702563 |
| ISSN | 21593450 |
| e-ISBN | 9781457702556 |
| DOI | 10.1109/TENCON.2011.6129270 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-21 |
| Publisher Place | Indonesia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electroencephalography Brain modeling Reliability Computers Neurophysiology Sleep Feature Extraction Electroencephalogram EEG Cognition Modeling Stimuli Augmented Cognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Computer Science Applications |
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