Decoding Generic Visual Representations From Human Brain Activity using Machine Learning

TL;DR

This paper evaluates various machine learning models and similarity metrics for decoding visual representations from human brain activity, aiming to improve accuracy and establish a reproducible baseline.

Contribution

It provides an extensive comparison of models and metrics for neural decoding of visual representations, highlighting key insights and setting a foundation for future research.

Findings

Different models vary in decoding accuracy

Similarity metrics significantly impact results

Provides a reproducible baseline for future studies

Abstract

Among the most impressive recent applications of neural decoding is the visual representation decoding, where the category of an object that a subject either sees or imagines is inferred by observing his/her brain activity. Even though there is an increasing interest in the aforementioned visual representation decoding task, there is no extensive study of the effect of using different machine learning models on the decoding accuracy. In this paper we provide an extensive evaluation of several machine learning models, along with different similarity metrics, for the aforementioned task, drawing many interesting conclusions. That way, this paper a) paves the way for developing more advanced and accurate methods and b) provides an extensive and easily reproducible baseline for the aforementioned decoding task.