Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method

• Main Authors: Hammad Nazeer, Noman Naseer, Aakif Mehboob, M. Jawad Khan, Rayyan Azam Khan, Umar Shahbaz Khan, Yasar Ayaz
• Format: Article
• Language: English
• Published: MDPI AG 2020-12-01
• Series: Sensors
• Subjects: functional near-infrared spectroscopy; brain–computer interface; z-score method; channel selection; region of interest; channel of interest
• Online Access: https://www.mdpi.com/1424-8220/20/23/6995

A state-of-the-art brain–computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel’s correlation coefficients’ maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional <i>t</i>-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (<i>p</i> < 0.0167) classification accuracies of 87.2 ± 7.0%, 88.4 ± 6.2%, and 88.1 ± 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the <i>t-</i>value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems’ performance.