AMMACHI Labs researchers Gayathri Manikutty, along with MSc. Cognitive Science, Learning & Technology students Arjun Venugopal, Ajeetha Rajan (Intern, AMMACHI Labs), and Varnika K (Intern, AMMACHI Labs), participated in an intensive EEG Workshop hosted by IIT Madras. The three-day event explored how Artificial Intelligence (AI) and Deep Learning can unravel the complexities of brain activity and cognitive processing.
Day 1 focused on building a solid foundation in EEG cap setup and safety protocols, signal acquisition, and preprocessing techniques such as filtering and Independent Component Analysis (ICA) using MNE Python. Participants also gained hands-on experience in feature extraction and applying Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models for brain signal classification.
Day 2 transitioned from theory to practical applications, covering motor and visual imagery, biometric identification, and clinical EEG use cases including epilepsy detection. Live demonstrations of EEG cap setup and real-time signal acquisition offered participants a closer look at the operational intricacies of EEG technology.
Day 3 delved into advanced clinical and cognitive domains, highlighting AI-driven seizure detection workflows, EEG biomarkers for Parkinson’s and Alzheimer’s, and decoding brain functions through gamma oscillations, MEG/EEG integration, and machine learning approaches.
The workshop, curated by Prof. Srinivasa Chakravarthy, the Neurogati team, and esteemed speakers at IIT Madras, provided a unique hands-on learning experience that seamlessly blended neuroscience, signal processing, and AI — guiding participants from raw EEG data to real-world diagnostic and brain–computer interface (BCI) applications.
Armed with this expertise, the AMMACHI Labs team plans to integrate these advanced EEG and AI methodologies into their ongoing research in neuro-skill learning, vocational skill training, and stroke/ABI rehabilitation — marking another step forward in harnessing technology for social good.
