Anatomical Pattern Analysis for Decoding Visual Stimuli in Human Brains

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.

Voxel selection framework based on meta‐heuristic search and mutual information for brain decoding

“…Which indicate the superior accuracy rate of proposed framework; in most cases except one. Furthermore, MFWVS exceeds all other methods except 5 for DS107 with 1% variation as shown in Table 3. The number of selected voxels has not been reported in the mentioned references, so we did not put them in Table 3 for comparison.…”

“…The comparison criteria are the number of selected voxels (without considering the number of time points in each voxel), selection method, and accuracy rate. All rows in the Table are inspired from Reference but the main concern of comparison is the voxel selection method.…”

“…The validation scheme is used to assess brain decoder for intersubject case, leave‐one‐subject‐out cross‐validation schemes; The state‐of‐art frameworks to select informative voxels from the fMRI images have been presented in References . The experimental results indicate how the stability of brain decoders is increased while over‐fitting avoided.…”

“…1 In fMRI data, it is difficult to distinguish between neural patterns due to the spontaneous fluctuations, even if they are obtained from the same person. 2 The validation scheme is used to assess brain decoder for intersubject case, leave-one-subject-out cross-validation schemes; The state-of-art frameworks to select informative voxels from the fMRI images have been presented in References [3][4][5] . The experimental results indicate how the stability of brain decoders is increased while over-fitting avoided.…”

Intelligent analysis of brain images

“…针对上述问题, 我们提出基于结构模式分析的方法 (anatomical pattern analysis, APA) 对人类大 脑中的视觉刺激进行解码 [47] . 该方法采用了一种全新的特征提取框架, 即基于大脑生理分区结构的 特征提取框架.…”