Brain Computer Interface: Classification of EEG for Left and Right Wrist Movements using AR Modeling and Bhattacharya Distance

Muhammed Shanir; Wassem Raza; David Powers

doi:10.1109/ISSNIP.2011.6146530

Brain Computer Interface: Classification of EEG for Left and Right Wrist Movements using AR Modeling and Bhattacharya Distance

Muhammed Shanir, Wassem Raza, David Powers

Research output: Contribution to conference › Paper › peer-review

4 Citations (Scopus)

Abstract

This paper presents classification of wrist movements (Left and Right) using Autoregressive modeling (AR). Here the features were extracted from Electro Encephalographic signals and AR modeled using Burg method. The simulation results show that by using AR modeling classification of Left and Right wrist movements can be classified with accuracy up to 98.75% and 96.8% respectively for executed and imaginary movements.

Original language	English
Pages	7-10
Number of pages	4
DOIs	https://doi.org/10.1109/ISSNIP.2011.6146530
Publication status	Published - 1 Dec 2011
Event	International Conference on Intelligent Sensors, Sensor Networks and Information Processing - Duration: 6 Dec 2011 → …

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Conference	International Conference on Intelligent Sensors, Sensor Networks and Information Processing
Period	6/12/11 → …

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10.1109/ISSNIP.2011.6146530

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title = "Brain Computer Interface: Classification of EEG for Left and Right Wrist Movements using AR Modeling and Bhattacharya Distance",

abstract = "This paper presents classification of wrist movements (Left and Right) using Autoregressive modeling (AR). Here the features were extracted from Electro Encephalographic signals and AR modeled using Burg method. The simulation results show that by using AR modeling classification of Left and Right wrist movements can be classified with accuracy up to 98.75% and 96.8% respectively for executed and imaginary movements.",

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AU - Raza, Wassem

AU - Powers, David

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N2 - This paper presents classification of wrist movements (Left and Right) using Autoregressive modeling (AR). Here the features were extracted from Electro Encephalographic signals and AR modeled using Burg method. The simulation results show that by using AR modeling classification of Left and Right wrist movements can be classified with accuracy up to 98.75% and 96.8% respectively for executed and imaginary movements.

AB - This paper presents classification of wrist movements (Left and Right) using Autoregressive modeling (AR). Here the features were extracted from Electro Encephalographic signals and AR modeled using Burg method. The simulation results show that by using AR modeling classification of Left and Right wrist movements can be classified with accuracy up to 98.75% and 96.8% respectively for executed and imaginary movements.

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Brain Computer Interface: Classification of EEG for Left and Right Wrist Movements using AR Modeling and Bhattacharya Distance

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