标题：
Anatomical Pattern Analysis for Decoding Visual Stimuli in Human Brains
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所属单位：
计算机科学与技术学院/人工智能学院/软件学院
发表刊物：
COGNITIVE COMPUTATION
关键字：
Brain decoding Multi-voxel pattern analysis Anatomical feature extraction Visual object recognition
摘要：
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号：
1866-9956
是否译文：
否
发表时间：
2018-04-01
合写作者：
张道强
通讯作者：
YOUSEFNEZHAD MUHAMMAD
发表时间：
2018-04-01
学位：工学博士学位
所在单位：计算机科学与技术学院/人工智能学院/软件学院