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Deep Learning

Deep Electrode Mapper: Localizing EEG Electrodes from 3D Point Clouds Using Deep Learning

Accurate EEG source localization depends on precise electrode coordinates, yet current methods are often manual, costly, and technically demanding. Deep Electrode Mapper attempts to address this by applying deep learning to segment electrodes from 3D head models—derived from MRI or 3D scans—and localize their coordinates using clustering. Although the project remains incomplete, it demonstrates a proof-of-concept pipeline, and progress is documented in the public repository.

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Evaluating ANNs of the Visual System with Representational Similarity Analysis

This project aims to evaluate the similarity between the representations of artificial neural networks (ANNs) and the visual system in the mouse brain using Representational Similarity Analysis (RSA).

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An introduction to brain decoding and comparing the results of the seven different classifier on Haxby dataset

Brain decoding is a neuroscience field that concerned about different types of stimuli from information that has already been encoded and represented in the brain by networks of neurons. My goal for this project is learning the fundamentals of brain decoding. Moreover, I compared the performance of seven different common classification approaches including Naive Bayes, Nearest Neighbours, Neural Networks, Logistic Regression, Support vector machine, Decision tree and finally the Artificial Neural Network on Haxby dataset.

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