EEG

Objective: As one branch of human-computerinteraction, affective Brain-Computer Interfaces (aBCI) interpretand utilize electroencephalogram (EEG) signalsto achieve real-time monitoring and recognition of individualemotional states, opening new possibilities foremotion-aware technologies and applications. However,the challenge of individual differences in EEG emotiondata severely constrains the effectiveness and generalizationcapability of existing models.

EEG Data Acquisition Using the Muse Device: Meditation and Rest Stages of Participants

This study aimed to investigate electroencephalogram (EEG) patterns during meditation to gain insights into the distinctions among practitioners with differing levels of experience. We analyzed EEG data from a cohort of seven participants with a unique background in Vipassana meditation, in order to discern how disparities in experience levels manifest in their neural activity. 

This dataset consists of electroencephalography (EEG) data from 10 healthy participants aged between 24 and 38 years with a mean age of 30 years (standard deviation 5 years). Five participants are male, and all the participants are right-handed.

Nowadays, more and more machine learning models have emerged in the field of sleep staging. However, they have not been widely used in practical situations, which may be due to the non-comprehensiveness of these models' clinical and subject background and the lack of persuasiveness and guarantee of generalization performance outside the given datasets. Meanwhile, polysomnogram (PSG), as the gold standard of sleep staging, is rather intrusive and expensive. In this paper, we propose a novel automatic sleep staging architecture calle