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Yalung, Christofer N, Al-Majeed, Salah 
ORCID: 0000-0002-5932-9658 and Karam, Jalal
(2016)
Analysis and Interpretation of Brain Wave Signals.
In: ICC 2016, March 22 - 23, Cambridge, UK.
Abstract
Brainwave Computer Interface (BCI) application has the potential to improve the quality of life for disabled patients and overall improvement of human thought concentration. In this paper, BCI is implemented using NeuroSky’s EEG biosensor. Brain wave signal analysis is presented through the consideration of a noisy environment to simulate a BCI in real world application. A total of 256 data points are acquired in each thought. The data are documented using MATLAB software via Bluetooth. A real time recording is implemented with different captured thoughts among seven participants. The standard deviation of the Mean Sample Value (MSV) and Value Above Zero(VAZ) shows high variation for the thought of backward, forward, left and move in comparison of each trial. The VAZ rate and Zero Crossing Rate (ZCR) have very minimal standard deviation in comparison of each trial. This shows that the environment could affect the concentration of the signals. The average of the results of each thought is also presented, in which each thought has distinct characteristics among other thoughts. This means that classification is possible even noise or interruption is present in the surroundings and wireless transmission is utilized. The total number of peak points was recorded in each EEG sample. Also, the correlation coefficients among three participants having the same tasked were analyzed.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Best paper award. |
| Uncontrolled Keywords: | BCI, NeuroSky; Feature Extraction; Noisy Environment; Peak Points |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Susan Turner |
| Date Deposited: | 04 Oct 2018 16:16 |
| Last Modified: | 31 Aug 2023 08:01 |
| URI: | https://eprints.glos.ac.uk/id/eprint/6046 |
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