Brain Controlled Wheelchair to Navigate in Familiar Environments
This thesis was done by Brice Rebasmen , national university of singapore, 2009. He proposed an hybrid PCI based on P300 EEG brain interface, which is relatively low cost.He devise a strategy to control the motion of the wheelchair in predefined paths,and user can select the destination menu. He tested the wheelchair in indoor environment within a reasonable time.
Brain Controlled Wheelchair using LabVIEW
This Project report is done for bachelor degree by Agneev Guin and Bidyut Bikash Baishya,from SRM university.The proposed a method based on eyeblink ,they use an single electrode system based on neurosky mindwave mobile.so user can select the destination thru eyeblink software
Mind Controlled Wheelchair
This paper was presented by Utkarsh Sinha and Kanthi.M ,They proposed method based on portable EEG headset, MCU and software Graphical user Interface for Control processing together facilitate the movement of the wheelchair by processing the user’s brain activity and eye blinks’ frequency.
Developing Intelligent Wheelchairs for the Handicapped
This report does not use an EEG but explains about the smart wheel chair of Handicapped for automatic navigation with intelligence, The report is included to gets some research perspective on designing wheel chairs which could be incorporated with the EEG based wheelchair.
Click here to download the report for Intelligent Wheelchairs for the Handicapped
EEG Signal Classification For Wheelchair Control Application
This is the master thesis done by ROZI ROSLINDA BINTI ABU HASSAN,Faculty of Electric and Electronic Engineering ,Universiti Tun Hussein Onn Malaysia In this project, the movement of wheelchair (left, right, forward and reverse) will classified by user focusing based on four visible picture in various shape and colour also four non-visible picture (used thought image),Visually evoked eeg signals, that represent the movement. EEG signal were analyzed to find out the features by using Fast Fourier Transform (FFT). This project used alpha and beta band to collect the data. The analysis have made based on the peak and average value which then be compared to define the most significant differentiation between signals.
Fast Brain Control Systems for Electric Wheelchair using Support Vector Machine
Widodo Budiharto and team created a software to process electroencephalography (EEG) data using Emotiv’s Software Development Kit (SDK). The propose a mothod to increase the accuracy rate of the brain control system by applying Support Vector Machine (SVM) . EEG samples are taken from several respondent with disabilities but still have healthy brainto pick most suitable EEG channel which will be used as a proper learning input in order to simplify the computational complexity. The controller system based on Arduino microcontroller and combined with our software to control the wheel movement. The result of this research is a brain-controlled electric wheelchair with enhanced and optimized EEG classification.
Smart Wheelchair for Differently-Abled People Using Bio-Signals
This is bachelor thesis submitted by Umang Yadav and team . The idea is based on eyeblink whenever we wink or close our eyes, our eyeball moves in the upward direction in the socket and thus a substantial potential difference is obtained in our EEG pattern thereby making the data set computable for further calculations.