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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 23; p. 6995
• Main Authors: Nazeer, Hammad, Naseer, Noman, Mehboob, Aakif, Khan, Muhammad Jawad, Khan, Rayyan Azam, Khan, Umar Shahbaz, Ayaz, Yasar
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.12.2020; MDPI
• Subjects: Accuracy; Algorithms; brain–computer interface; channel of interest; channel selection; Classification; Datasets; Electroencephalography; functional near-infrared spectroscopy; Machine learning; Medical imaging; Methods; Noise; Open access; Principal components analysis; region of interest; Values; z-score method; channel selection; brain–computer interface; channel of interest; z-score method; functional near-infrared spectroscopy; region of interest
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20236995

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 t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p < 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 t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems’ performance.