R.Kottaimalai 1, J.Goldwyn Sudhakar 2,
T.ElizabethRani3
1 Assistant Professor. Dept of ECE ,Sree Sowdambika College of Engineering, Aruppukottai, India
2 Assistant. Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.
3 Asstistant Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.
1 Assistant Professor. Dept of ECE ,Sree Sowdambika College of Engineering, Aruppukottai, India
2 Assistant. Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.
3 Asstistant Professor. Dept of EIE, Sree Sowdambika College of Engineering, Aruppukottai, India.
The people who have lost
movement or language function due to traffic accidents or neuromuscular
disease, necessitating large numbers of care assistants to support severely
disable patients who have almost no voluntary control of body movements.
Brain-Computer interface (BCI) is a direct communication pathway between a
human brain and an external device. Such systems permit people to communicate
through direct measurements of brain activity, without requiring any movement.
The task of the BCI is to identify and predict behaviorally induced changes or
cognitive states in a user‘s brain signals. Brain signals are recorded from
electrodes placed on the scalp. In this report the Soft Computing technique
like ANFIS was applied on the EEG signals which are taken during six limb
movement tasks and also during six imaginary tasks, and the data are taken for
classification. From the test results it is observed that the probability of
correct classification has been increased for the limb movements by using ANFIS
and for feature extraction we use wavelet transform.
