Artifact removal from EEG using ANFIS-GA
| Autor: | Leroy F. Pereira, Supriya A. Patil, Chandrakant D. Mahadeshwar, Ishita Mishra, Llewellyn D'Souza |
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| Rok vydání: | 2016 |
| Předmět: |
Artifact (error)
Adaptive neuro fuzzy inference system medicine.diagnostic_test Computer science 020209 energy Speech recognition 020206 networking & telecommunications 02 engineering and technology Electroencephalography Adaptive system Genetic algorithm 0202 electrical engineering electronic engineering information engineering medicine Hybrid learning algorithm Global optimization Active noise control |
| Zdroj: | 2016 Online International Conference on Green Engineering and Technologies (IC-GET). |
| DOI: | 10.1109/get.2016.7916726 |
| Popis: | Electroencephalogram (EEG) is the measurement of the electrical activity of the brain from the scalp. EEG is corrupted with various artifacts such as Electroocculogram(EOG) and Electromyogram (EMG) which originate from different sites other than the brain. In this paper we propose an Adaptive Noise Cancellation method(ANC) called Adaptive Neuro?Fuzzy Inference System(ANFIS), where a global optimization technique specifically Genetic Algorithm(ANFIS-GA) has been used to optimize the parameters of the ANFIS structure. This paper shows that the proposed method, ANFIS-GA eliminates the EOG artifact from the EEG and surpasses the performance as compared to the Hybrid Learning Algorithm that has been employed generally to tune the parameters of the ANFIS structure. A comparative study has been done on ANC implemented using ANFIS and ANFIS - GA. |
| Databáze: | OpenAIRE |
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