Decoding Generic Visual Representations From Human Brain Activity using Machine Learning
This work addresses the need for better methods in neural decoding for visual tasks, but it is incremental as it focuses on comparing existing models rather than introducing new ones.
The paper tackles the problem of decoding visual representations from human brain activity by evaluating various machine learning models and similarity metrics, concluding with insights to improve accuracy and providing a reproducible baseline.
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.