Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images

TL;DR

This paper introduces Anatomical Pattern Analysis (APA), a novel framework that improves decoding of visual stimuli from fMRI data by enhancing feature extraction, handling data imbalance, and enabling multi-class prediction, outperforming existing methods.

Contribution

The paper presents a new anatomical feature extraction method and an imbalance AdaBoost algorithm within APA, advancing multi-Voxels Pattern Analysis for better brain decoding.

Findings

APA achieves superior accuracy over state-of-the-art methods

Effective detection of active brain regions for different stimuli

Enables combining datasets for improved classification

Abstract

A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noises in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.