Design of multiplayer attention based brain-computer interface game

This Final Year Project report records a summary of the design and implementation of multiple Brain Computer Interface(BCI) games as well as training interfaces. A total of two Training Interfaces and three attention-based game have been developed. The first training interface aims to differentiate...

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Bibliographic Details
Main Author: Khong, Alvin Farn Loong
Other Authors: Vinod Achutavarrier Prasad
Format: Final Year Project
Language:English
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/10356/58968
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Institution: Nanyang Technological University
Language: English
Description
Summary:This Final Year Project report records a summary of the design and implementation of multiple Brain Computer Interface(BCI) games as well as training interfaces. A total of two Training Interfaces and three attention-based game have been developed. The first training interface aims to differentiate visual attentiveness and inattentiveness. The second training interface aims to separate attention into three levels, specifically, "relax", "neutral" and "attentive". The aim of these training interface is to compute respective threshold which aids in the classification of EEG signals. These threshold will be utilised by the various Brain Computer Interface game. The BCI games developed varies greatly in its complexity, game play as well as cognitive elements involved. MindSprint, a two player game, allows player to compete against each other by controlling the running speed of their game character via visual attention. MindBall, an over the network up to four players game, allows player to compete against each other via different levels of attention. In addition, element of memory is included in the game play. Lastly, MindBattleField, a multiplayer game of up to eight players, combined all three cognitive elements, adding sophistication into the traditional First Person Shooting game. On top of that, various aspects of BCI game related topics are also explored and experimented. For example, agents programming with BCI, multi paradigm BCI as well as the review of existing game development tools.