I am inclined to research on EEG classification using ML/DL. The research area seems saturated. Hence, I am confused as to where I can contribute.
First of all, I want to encourage you not to give up on an area just because there are a lot of researchers in it. People should follow their interests if they are capable of managing the task and are interested in them. It's not only that EEG research is a promising field, but it's also interesting to classify EEG data using machine learning or deep learning approaches. It's okay if it seems saturated to you. Improving already completed work is always a way to contribute. There are many ways to propose improved algorithms and models if you have an interest for mathematical modelling. Remember that even in well explored research fields, there is always space for creativity and advancement.
It's better to start with a review paper on the latest research article in this field. In one paper (latest review paper), you can gain a clear idea of the work that has been done and the suggestions put forward by the authors (researchers) based on their investigation. This approach helps you understand the current state of the field and identify potential gaps or areas for further exploration.
In the biomedical field, preference should be given to applications that demonstrate effectiveness in promoting health and safety.
1. And, I would like to suggest that you integrate ML/DL techniques for EEG classification along with IoT or some real-time device, such as Jetson Nano or an equivalent.
2. EEG signals should have noise and limited spatial resolution. Maybe you can investigate.
3. Left and right hand movements generate distinct EEG signals. If you can collect a real dataset from reputable medical resources, you could investigate EEG signals in paralyzed individuals and analyze them.
I am sharing here some of the article maybe you can have a look, i feels that could help you better:
*) Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review.
Article Current Status, Challenges, and Possible Solutions of EEG-Ba...
*) A review on analysis of EEG signals.
https://ieeexplore.ieee.org/document/7164844
*) Deep Learning Algorithm for Brain-Computer Interface.
https://www.hindawi.com/journals/sp/2020/5762149/
*)Encyclopedia of Clinical Neuropsychology.
https://link.springer.com/referencework/10.1007/978-3-319-56782-2
Finally, as this is your graduation thesis, it's important to have a backup plan. During research, numerous byproducts are often produced, many of which hold value. I hope you will successfully reach your final destination with this research. However, it's essential to keep proper track of your byproducts. They may prove invaluable in shaping your thesis and ensuring you graduate on time. Furthermore, even after graduation, consider continuing your research if possible.