A study on multi-class anxiety detection using wearable EEG headband

In this paper, we present a trait anxiety detection framework using resting-state electroencephalography (EEG) data. Our proposed framework consists of EEG data acquisition, pre-processing, feature extraction and selection, and classification stages. EEG data of 65 participants is recorded in an eye-open state for the duration of two minutes. The trait anxiety scores are gathered using the state-trait anxiety inventory questionnaire, which is used to label the participant's EEG data into two (non-anxious and anxious) and three (non-anxious, low anxious, and highly anxious) classes. Pre-processing of the recorded EEG data is performed using the onboard noise cancellation scheme of the MUSE EEG headband. Channel selection is performed by applying a t-test and analysis of variance on the power spectral densities for two and three classes of anxiety respectively. Five time-domain features are extracted from the selected EEG channels. The wrapper method for feature selection is applied for selecting an optimum subset of features, which are used to classify the trait anxiety. The highest classification accuracy of 87.69% and 83.07% using random forest classifier is achieved for two and three class anxiety classification respectively. Our proposed trait anxiety detection scheme outperforms existing schemes in terms of higher classification accuracy and reduced feature vector length.