The problem NeuroSense solves

The alarming rise in workplace stress affects a significant number of office-going staff, with studies reporting that approximately 80% of employees experience stress-related symptoms. This chronic stress not only impacts their mental health but also results in decreased productivity, higher absenteeism rates, and increased healthcare costs. Therefore, there is an urgent need for an innovative solution that can effectively detect and manage stress, improving the overall well-being and performance of individuals in the workplace.

Challenges we ran into

Complex EEG Data Interpretation:
One of the significant challenges was interpreting the raw EEG data collected from the wearable device. EEG signals are intricate and require careful preprocessing to extract meaningful patterns. We had to delve deep into signal processing techniques to clean, filter, and transform the data into usable forms for our machine learning models.

Model Calibration and Generalization:
Building accurate machine learning models for stress prediction presented its own set of challenges. The models had to be calibrated to recognize subtle stress indicators across various individuals and situations. Achieving a balance between model sensitivity and specificity was a meticulous process.

Real-Time Processing Demands:
Processing EEG data in real-time was another hurdle. Real-time analysis demands efficient algorithms and computing power. We had to optimize our software to handle incoming data streams promptly while ensuring accurate stress level predictions.

Mobile App-Device Integration:
Integrating the mobile app with the wearable device and software analysis was complex. Ensuring seamless synchronization between the hardware and software components required meticulous testing and debugging.