EEG-based mental workload recognition related to multitasking

Mental workload can be recognized from Electroencephalogram (EEG) and can be used to assess mental efforts of the user performing different tasks. In this work, we designed and implemented an experiment for mental workload recognition related to no-task, visual task, auditory task and multitask performance. The Simultaneous Capacity SIMKAP test was used to induce different levels of mental workload related to multitasking in 12 subjects. EEG data was collected with Emotiv device, processed and analyzed using power, statistical, fractal dimension (FD) features with Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classifiers. The best accuracy of 90.39% for 2 classes and 80.09% for 4 classes using SVM was achieved when statistical and FD feature combination was used. The proposed algorithm can be applied for mental workload monitoring.