Neurofeedback system for training attentiveness

• Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Main Author: Lee K.Y.; Hidzir E.E.; Haron M.R.
• Format: Conference paper
• Language: English
• Published: Springer Verlag 2017
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018525244&doi=10.1007%2f978-3-319-54430-4_33&partnerID=40&md5=759375a566e58809485c56590f460cf1

Attention Deficit Disorder (ADD) has long been recognized as a public health concern amongst children, where its symptoms include impulsiveness, inattentiveness and unfocused. The consequence is children with poor academic performance and discipline that has negative impact on their future. Current treatment for ADD uses powerful psycho-stimulant drugs, to reduce aggression and enhance concentration. However, there are always risk factors and adverse effects with these drugs. Moreover, drugs do not alter the dysfunctional condition. Forefront research in biomedical engineering unveils neurofeedback, which presents an exciting alternative approach to neural related disorders. Our ultimate goal is to develop a neurofeedback system to enable anyone with attention deficit to practice regulating their brain to reach an attentive state of mind, with reduced dependency on drug related intervention. Relying on neuroplasticity, neurofeedback focuses on the training of brain through activities to circumvent the dysfunctional condition. In this paper, such a system has been developed and applied on normal healthy subjects, to establish the protocol on EEG subband and electrode placement as well as system functional testing. It consists of a wireless EEG acquisition module, a feature extraction module, an IoT database module, an Intel Edison microcontroller board and a feedback activity center, the humanoid robot. The protocol on subband and electrode placement is established with short time Fourier transform (STFT) and fast Fourier transform (FFT). The system rewards the subject if the root mean square voltage of his beta subband at Fp1 exceeds the target voltage, when he is attentive. © Springer International Publishing AG 2017.