A Context-Supported Deep Learning Framework for Multimodal Brain Imaging Classification

• Published in: IEEE transactions on human-machine systems Vol. 49; no. 6; pp. 611 - 622
• Main Authors: Jiang, Jianmin, Fares, Ahmed, Zhong, Sheng-Hua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain research; Classification; Consistency tests; Deep learning; electroencephalogram (EEG); Electroencephalography; explicit learning modality; Feature extraction; Functional magnetic resonance imaging; Image classification; Image processing; implicit learning modality; Machine learning; Mapping; Medical imaging; Multimedia; object classification; Signal classification; Source code; Visual signals
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2019.2904615

Over the past decade, "content-based" multimedia systems have realized success. By comparison, brain imaging and classification systems demand more efforts for improvement with respect to accuracy, generalization, and interpretation. The relationship between electroencephalogram (EEG) signals and corresponding multimedia content needs to be further explored. In this paper, we integrate implicit and explicit learning modalities into a context-supported deep learning framework. We propose an improved solution for the task of brain imaging classification via EEG signals. In our proposed framework, we introduce a consistency test by exploiting the context of brain images and establishing a mapping between visual-level features and cognitive-level features inferred based on EEG signals. In this way, a multimodal approach can be developed to deliver an improved solution for brain imaging and its classification based on explicit learning modalities and research from the image processing community. In addition, a number of fusion techniques are investigated in this work to optimize individual classification results. Extensive experiments have been carried out, and their results demonstrate the effectiveness of our proposed framework. In comparison with the existing state-of-the-art approaches, our proposed framework achieves superior performance in terms of not only the standard visual object classification criteria, but also the exploitation of transfer learning. For the convenience of research dissemination, we make the source code publicly available for downloading at GitHub (https://github.com/aneeg/dual-modal-learning).