Name
Abstract | ||
|---|---|---|
Concentration is an important part of our life especially during learning or thinking. Visually or auditory evoked concentration affects information processing in human brain. To understand the concentration process of humans, the underlying neural mechanism needs to be explored. EEG device is a promising device to understand underlying neural mechanism of various cognitive functions. In this paper, we propose an accurate concentration monitoring method using a low cost EEG device. Our low cost EEG device has two channel electrodes (FP1, FP2). Usually small channel EEG devices face filtering problem because commonly used filtering method, such as ICA, fails with less number of electrodes. In our work, we investigate effective filters for removing noises from raw data and suitable features for monitoring the concentration status with the low cost EEG device in real time. We collect EEG data from 10 participants for rest state with open eyes and concentration task state. For concentration task, Sudoku game is used. Using support vector machine, we successfully distinguish between rest state and concentration state over 88 % accuracy in real time. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26561-2_7 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
EEG,Concentration,Power spectral density,Support vector machine,Hurst exponent | Information processing,Pattern recognition,Computer science,Support vector machine,Filter (signal processing),Communication channel,Filtering problem,Spectral density,Artificial intelligence,Cognition,Electroencephalography | Conference |
Volume | ISSN | Citations |
9492 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun-Su Kang | 1 | 7 | 2.99 |
| Amitash Ojha | 2 | 18 | 5.60 |
| Minho Lee | 3 | 4 | 3.16 |