A Hybrid Approach for Extracting EMG signals by Filtering EEG Data for IoT Applications for Immobile Persons

Brain Computer interface (BCI) is an emerging technology which empowers human to regulate the computer or other electronic gadgets with brain signals. This paper presents an electroencephalography (EEG) based BCI system with filtered electromyographic (EMG) signals for automating the home appliances. EEG signals are usually contaminated by various noise or artifacts which have to be removed in order to correctly interpret the desired output. The system focuses on extracting the EMG signals generated from the hand movement which can be used by a cripple, paraplegic, lame, paralyzed or a person with special need to enhance their independence and increase their capabilities. EEG signals are recorded and filtered out using hybrid digital filters. In this work, the filtered signals are sent to the micro-controller to operate different devices.

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