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A universal unanswered question in neuroscience and machine learning iswhether computers can decode the patterns of the human brain. Multi-VoxelsPattern Analysis (MVPA) is a critical tool for addressing this question.However, there are two challenges in the previous MVPA methods, which includedecreasing sparsity and noises in the extracted features and increasing theperformance of prediction. In overcoming mentioned challenges, this paperproposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in thehuman brain. This framework develops a novel anatomical feature extractionmethod and a new imbalance AdaBoost algorithm for binary classification.Further, it utilizes an Error-Correcting Output Codes (ECOC) method formulti-class prediction. APA can automatically detect active regions for eachcategory of the visual stimuli. Moreover, it enables us to combine homogeneousdatasets for applying advanced classification. Experimental studies on 4 visualcategories (words, consonants, objects and scrambled photos) demonstrate thatthe proposed approach achieves superior performance to state-of-the-artmethods.

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