Abstract: This project explores the novel use of transformer neural networks to assess the mental stability of corporate workers based on electroencephalogram (EEG) data. With rising stress levels and mental health issues among corporate workers, there is a pressing need for sophisticated tools that can track and sustain employee well-being.
Transformers, with their better capacity to handle sequential data, offer a promising method for EEG signal analysis, which is sequential and complex in nature. The research proposes to develop a strong and interpretable model that can detect patterns in EEG data reflecting mental well-being or distress. Transformers have the potential to provide more accurate and detailed insights by extracting long-range dependencies in the temporal dynamics of brain signals compared to conventional models.
The workflow of the project involves a number of key steps. First, raw EEG data preprocessing to eliminate noise and artifacts so that the data is reliable and of good quality. Then the cleaned data from which features related to the aspects most representative of mental health are extracted. The backbone of the project is to train a transformer neural network on this feature-rich dataset so that the model can learn and recognize complex patterns that could reflect different mental states.
The long-term aim is to create a tool that can be fully integrated into corporate wellness initiatives. This tool has the potential to offer actionable insights, enabling businesses to act preemptively on mental health concerns and promote employees' overall well-being. Through the use of state-of-the-art technology, this study not only seeks to improve individual health but also to foster a healthier, more productive workplace. The project underscores the vital importance of innovative technologies in encouraging mental health consciousness and intervention within the business community.
Keywords: Electroencephalogram (EEG), Mental stability, Transformer neural network, Feature extraction, Temporal dynamics, Corporate wellness, Employee suppor
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