首页|Reports from Oslo Metropolitan University (OsloMet) Advance Knowledge in Machine Learning (Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface)
Reports from Oslo Metropolitan University (OsloMet) Advance Knowledge in Machine Learning (Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on artificial intelligence. According to news reporting out of Oslo, Norway, by NewsRx editors, research stated, "In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers." The news reporters obtained a quote from the research from Oslo Metropolitan University (OsloMet): "Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated D HbO and deoxygenated D HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named ‘Hemo-Net' has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data."
Oslo Metropolitan University (OsloMet)OsloNorwayEuropeCyborgsEmerging TechnologiesMachine Learning
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