Anatomical Pattern Analysis for decoding visual stimuli in human brains

TL;DR

This paper introduces Anatomical Pattern Analysis (APA), a novel framework that enhances decoding of visual stimuli from human brain patterns by improving feature extraction and classification methods, outperforming existing techniques.

Contribution

The paper presents a new anatomical feature extraction method and an imbalance AdaBoost algorithm, along with ECOC for multiclass prediction, advancing MVPA in brain decoding.

Findings

APA achieves superior accuracy compared to state-of-the-art methods.

The framework effectively detects active brain regions for different visual categories.

Combining datasets improves classification performance.

Abstract

Background: 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 noise in the extracted features and increasing the performance of prediction. Methods: 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 multiclass 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. Results and Conclusions: 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.