Title :
HMM based predictive model of brain computer interface
Author :
Bansal, D. ; Sarkar, A.
Author_Institution :
Web & Services, Samsung R&D Inst., Bangalore, India
Abstract :
This paper, for effective interaction between user´s brain and computer, proposes a Hidden Markov Modelbased prediction approach wherein based on its current state of action, the system calculates the possible outcomes that would lead to the next state/action generated from Hidden Markov Models themselves. These Hidden Markov Models are trained through HMM Toolkit using the frequency features extracted from input EEG waves in the training phase. In the data prediction phase, three sets of ten input EEG waves for different tasks obtained from the end user are compared with the actual training wave data and next state of action that user wants to perform is predicted based on the probability distribution over the possible output tokens of Hidden Markov Models from the training phase. For e.g. for task in which user thinks of opening a music player, on basis of this EEG wave, wave corresponding to playing a song is predicted and system performs it on its own.
Keywords :
brain-computer interfaces; electroencephalography; hidden Markov models; EEG waves; HMM based predictive model; HMM toolkit; brain computer interface; data prediction phase; hidden Markov model based prediction approach; probability distribution; Brain models; Computers; Context; Electroencephalography; Feature extraction; Hidden Markov models; Brain Computer Interface; Context dependent EEG units; Electroencephalograph; HMM Toolkit; Hidden Markov Models;
Conference_Titel :
India Conference (INDICON), 2014 Annual IEEE
Conference_Location :
Pune
Print_ISBN :
978-1-4799-5362-2
DOI :
10.1109/INDICON.2014.7030654