Improved Classification Accuracy of Four Class FNIRS-BCI

Experimentation and analysis in brain-computer interface (BCI) has increasingly been receiving quite some consideration as a substitute communication possibility for patients who are severely paralyzed in the last few years. To measure brain activities using optical signals a fairly new and non-invasive brain imaging tool can be put to test know as Functional near-infrared spectroscopy (fNIRS). Comparability low cost, safety, portability and wear ability are some of the main advantages of imaging of brain using this non-invasive modality. We propose in this paper to apply this relatively new non-invasive fNIRS technique to make an image of brain activities during four different mental tasks. These tasks include Mental Arithmetic (MA), Motor Imagery (i.e. Left-Hand and Right-Hand Motor Imagery) and Rest. fNIRS data used is an open access dataset which was collected by Continuous-wave imaging system (NIR Scout NIRx GmbH, Berlin, Germany) with the sampling frequency of 10 Hz. The research we have done in this paper Data synchronization is performed before the data is preprocessed. After the preprocessing and signal analysis of data our results shows hemodynamic behavior of multiple patterns during the tasks performed. These unique patterns of hemodynamic behavior can be used to differentiate and distinguish different task. We were able to compare, differentiate and distinguish the brain signal activities captured while performing 4 different tasks using 3 different classifiers i.e. Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The average classification accuracy of above 90% is achieved by using K Nearest Neighbors (KNN).