The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.
This paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.
At the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.
BCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.
Quality assessment and reliable KPIs were something that the authors targeted to achieve at the beginning of the project, and the authors are most thankful to Suyash Dixit for his insightful comments along with reviewing the work. The authors also would like to thank Ms. Nishtha for her assistance, the generosity and expertise that helped us to improve the research in innumerable ways and has pushed us to excel in our many trials.
Shankhdhar, A., Verma, P.K., Agrawal, P., Madaan, V. and Gupta, C. (2022), "Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography", International Journal of Quality & Reliability Management, Vol. 39 No. 7, pp. 1676-1703. https://doi.org/10.1108/IJQRM-07-2021-0237
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