Deciphering Minds and Motion: A Unified Exploration of Brain Signal Decoding and Activity Recognition Through AI-driven Analysis
Arafat Rahman, Ayontika Das, Anindya Nag, Tamanna Zubairi Sana · River Publishers eBooks · 2025
Human activity recognition (HAR) and machine learning approaches for brain signal decoding represent key intersections of technology and human behavior analysis. HAR involves automated detection of human actions through sensor data, with applications in healthcare, sports, and smart homes. Machine learning for brain signal decoding interprets brain signals, providing insights into cognition and neurological processes. Both fields face challenges due to the complexity of human movement and brain activity, requiring sophisticated algorithms and sensors for accurate analysis. Despite significant progress, selecting optimal algorithms and sensors for specific applications remains challenging. This study aims to bridge this gap by investigating effective machine learning strategies for accurately identifying human activities and decoding brain signals. It will examine the use of vision-based and 312 non-vision-based acquisition devices in various contexts. The applications of HAR extend to environmental surveillance, security, and beyond, where accurate human activity detection is crucial. Similarly, brain signal decoding has profound implications for understanding and treating neurological conditions. This comprehensive analysis will highlight the current state and future prospects of these dynamic fields, contributing to advancements in neuroscience and artificial intelligence.