Cognitive load and emotional states may impact the cognitive learning process. Detection of reliable emotions and cognitive load would benefit the design of the emotional intelligent mobility system for visually impaired peoples (VIPs). Application of learning process using electroencephalography (EEG) offers novel and promising approaches to measure cognitive load and emotional states. EEG is used to identify the physiological index that can lead to detecting cognitive load and emotions which can help to explore the knowledge of learning processes. Basically, EEG is a record of ongoing electrical signals to represent the human brain activity due to external and internal stimuli. Therefore, in this study EEG signals are captured from participants with nine different degrees of sight loss people. EEG signals are then used to measure various cognitive load and emotional states to evaluate cognitive learning process for the VIPs. To support the argument of cognitive learning process, the complexity of the tasks in terms of cognitive load and emotional states are quantified considering diverse factors by extracting features from various well-established metrics such as permutation entropy, event related synchronization/desynchronization, arousal, and valence when VIPs are navigating unfamiliar indoor environments. A classification accuracy of door is 86.67% which is achieved by the proposed model. It has almost 10% of improvement compared to another state-of the-art method who have used same dataset. Moreover, we have achieved 10% and 1% more accuracy in the corridor and open space conditions compared to the existing method. Experimental results also demonstrated that learning process is significantly improved considering wide range of obstacles when they are navigating indoor environments.
An Algorithm for Extracting Entropy Features from EEG Signals Based on T-test and KPCA and Its Application on Driving Fatigue State Recognition