Title of Paper:Anatomical Pattern Analysis for Decoding Visual Stimuli in Human Brains

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Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:COGNITIVE COMPUTATION

Key Words:Brain decoding Multi-voxel pattern analysis Anatomical feature extraction Visual object recognition

Abstract:A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxel 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. 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. Experimental studies on four visual categories (words, consonants, objects, and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.

ISSN No.:1866-9956

Translation or Not:no

Date of Publication:2018-04-01

Co-author:zdq

Correspondence Author:YOUSEFNEZHAD MUHAMMAD