Over the years, the usage of artificial intelligence (AI) algorithms is increased to develop various smart applications using Internet-of-Things. Home automation is a fast emerging area that involves monitoring and controlling of household appliances for user comfort and efficient management. Using mental commands to control different electrical appliances and objects in house is a very interesting application. Brain–Computer Interface is used to relay the information from the subject’s brain to an Electronic device, and such devices can be used for this purpose. The information from the subject’s brain is collected in form of Electroencephalogram (EEG) signals. In this paper, we analyze the use of EEG signals for applications related to home automation. We present a hybrid model which makes use of Long Short-Term Memory which is considered as a robust temporal classification model in AI and classical Random Forest Classifier for EEG classification. We also discuss how our proposed hybrid model overcomes the limitation presented by the individual models. To arrive at the best model, we have analyzed various parameters such as sampling rate and combination of different brain rhythms which we finally use in our hybrid model. Based on experiments conducted on a custom-built dataset, we also discuss the spatial significance of different electrodes of the EEG device and get insight in signals generated from different areas of the brain.
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The authors would like to acknowledge Mr. Tauqueer Ahmad, Mr. Shubham Kumar Pandey and Mr. Prakhar Pandey for their help in designing the protocol of the experiments.
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Roy, P.P., Kumar, P. & Chang, V. A hybrid classifier combination for home automation using EEG signals. Neural Comput & Applic 32, 16135–16147 (2020). https://doi.org/10.1007/s00521-020-04804-y
- Hybrid machine learning