Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN)

• Published in: Bioengineering (Basel) Vol. 6; no. 2; p. 46
• Main Authors: Muhammad, Yar, Vaino, Daniil
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.05.2019; MDPI
• Subjects: Activity recognition; Algorithms; ANN (artificial neural network); Arduino; Artificial neural networks; Back propagation networks; BCI (brain-computer interface); BFB (biofeedback); Bioengineering; bitronics; Brain; Brain research; Butterworth filters; Computer applications; Computer industry; Computer programs; Computers; Datasets; EEG; EEG (electroencephalography); Electrodes; Electroencephalography; Electrolytes; Electronic devices; Electronic equipment; Error correction; Experiments; Hardware; Human-computer interface; Implants; Interfaces; Learning theory; Literature reviews; Machine learning; Meditation; Nervous system; Neural networks; Neurons; New technology; Porous materials; Rhythm; Sensors; Software; Wheelchairs; ANN (artificial neural network); BCI (brain-computer interface); EEG (electroencephalography); machine learning; Arduino; BFB (biofeedback); bitronics
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering6020046

The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg–Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg–Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset.