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Mental Workload Classification

Mansi Sharma
Poster, 2021.


Passive Brain-computer Interfaces (BCI) are capable of estimating a user's cognitive state, mental workload, and are used in many other applications [5,6]. However, to make them usable in everyday life there are various challenges. One of them is large variability in the brain signals [4]. The given task in the Passive BCI Hackathon is to predict the mental workload (intra-subject estimation), where a subject performs three subtasks with the different levels of difficulties. BCI works in four main steps: Data Acquisition, Pre-processing and filtering, Feature extraction, and Classification and evaluation of the output. The provided pre-processed and epoched data is used for multiclass classification with easy, medium, and difficult conditions.

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