Thoughts, ideas, experiences and everything I learn along my journey.
My journey into neuroscience began with a simple personal interest in the brain. I was fascinated by how this complex organ shapes our thoughts, behaviour, memories, emotions, and perception. What started as curiosity gradually became a direction I wanted to pursue seriously.
Coming from a computer science and technology background, I initially approached problems through programming, data, and computational thinking. Over time, I realised that these skills could be powerful tools for understanding the brain. This intersection of technology, cognition, and neuroscience motivated me to pursue an MSc in Neural and Cognitive Science at the University of Hyderabad. During my MSc, I began exploring cognitive psychology, experimental research, and neuroscience methods. Working with behavioural paradigms such as Flanker, Go/No-Go, Task Switching, and N-back helped me understand how cognition can be studied experimentally. My exposure to EEG further strengthened my interest in understanding brain activity through measurable neural signals.
My research experiences also introduced me to areas such as sense of agency, consciousness, neurophysiology, and computational neuroscience. Working with tools such as PsychoPy, Lab Streaming Layer, MNE, and EEG datasets has helped me connect experimental neuroscience with technology and data analysis. Currently, my dissertation focuses on the Brain Age Index, in collaboration with the University of Hyderabad and NIMHANS. I am exploring how EEG signals and cognitive assessments can be combined with machine learning to understand healthy brain ageing and develop approaches for estimating brain age. Looking ahead, I hope to continue working at the intersection of neuroscience, computation, and technology.
My future interests include EEG, neurophysiology, brain imaging, computational neuroscience, brain-computer interfaces, and clinical neuroscience. I am still at the beginning of this journey, but the questions that first sparked my curiosity continue to drive me forward. For me, neuroscience is not just a field of study—it is a journey of continuously asking better questions about the brain.
When I first started learning about EEG, I was fascinated by the idea that we could record the electrical activity of the brain from sensors placed on the scalp. What initially seemed like a complex technical process slowly became one of the most interesting parts of my neuroscience journey.
Coming from a computer science background, I was familiar with programming and data, but EEG introduced me to a completely different kind of signal. I had to learn how brain activity is recorded, what electrodes measure, and how neural signals can be affected by noise, movement, eye blinks, and other artefacts. My learning process was not limited to theory. I started working with EEG experiments, PsychoPy, Lab Streaming Layer (LSL), and LabRecorder to understand how behavioural events and neural signals can be synchronised. I also began exploring tools such as MNE and PyXDF for loading, preprocessing, and analysing EEG data. One of the most important lessons I learned was that collecting EEG data is only the beginning. Good experimental design, proper electrode placement, synchronisation, preprocessing, and careful interpretation are all essential for meaningful results. Working with EEG has also changed the way I think about neuroscience.
A signal that initially looks like a complicated waveform can contain information related to cognition, behaviour, and brain function when analysed carefully. I am still learning, and there is a lot more to understand—from signal processing and feature extraction to machine learning and deep learning approaches for EEG. My current work with EEG and cognitive assessments for my Brain Age Index dissertation has given me an opportunity to apply what I have learned to a real research question. For me, learning EEG is not just about learning another technique. It is about learning how to listen to the brain through its signals.