Alzheimer’s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion

Zhang, Yuanpeng; Wang, Shuihua; Xia, Kaijian; Jiang, Yizhang; Qian, Pengjiang

doi:10.1016/j.inffus.2020.09.002

Cited by 112 publications

(49 citation statements)

References 30 publications

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“…Because the classification options are one-hot encoding multi-dimensional vectors, we used multi-classification cross-entropy as the loss function for all the models (de Boer et al, 2005 ). The Loss of the model can be calculated by the following formula (Zhang et al, 2021 ):…”

Section: Methodsmentioning

confidence: 99%

An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

et al. 2020

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Emotion is the human brain reacting to objective things. In real life, human emotions are complex and changeable, so research into emotion recognition is of great significance in real life applications. Recently, many deep learning and machine learning methods have been widely applied in emotion recognition based on EEG signals. However, the traditional machine learning method has a major disadvantage in that the feature extraction process is usually cumbersome, which relies heavily on human experts. Then, end-to-end deep learning methods emerged as an effective method to address this disadvantage with the help of raw signal features and time-frequency spectrums. Here, we investigated the application of several deep learning models to the research field of EEG-based emotion recognition, including deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM (CNN-LSTM). The experiments were carried on the well-known DEAP dataset. Experimental results show that the CNN and CNN-LSTM models had high classification performance in EEG-based emotion recognition, and their accurate extraction rate of RAW data reached 90.12 and 94.17%, respectively. The performance of the DNN model was not as accurate as other models, but the training speed was fast. The LSTM model was not as stable as the CNN and CNN-LSTM models. Moreover, with the same number of parameters, the training speed of the LSTM was much slower and it was difficult to achieve convergence. Additional parameter comparison experiments with other models, including epoch, learning rate, and dropout probability, were also conducted in the paper. Comparison results prove that the DNN model converged to optimal with fewer epochs and a higher learning rate. In contrast, the CNN model needed more epochs to learn. As for dropout probability, reducing the parameters by ~50% each time was appropriate.

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Section: Methodsmentioning

confidence: 99%

An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

et al. 2020

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“…In the process of recognizing the patient's intention, deep feature extraction of the collected EEG is required. CNN has stronger advantages than machine learning algorithms [22][23][24][25]. erefore, CNN [26][27][28] is used for feature extraction.…”

Section: Cnnmentioning

confidence: 99%

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

Shi

Yang

Zhang

2021

Mathematical Problems in Engineering

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With the emergence of the phenomenon of social aging, the elderly have frequent physical movement disorders. In particular, the movement disorder of the ankle joint seriously affects the daily life of the elderly. Rehabilitation robots are of great significance for improving the efficiency of rehabilitation, ensuring the quality of rehabilitation, and reducing the labor intensity of workers. As an auxiliary treatment tool, rehabilitation robots should have rich and effective motion modes. The exercise mode should be adaptable for patients with different conditions and different recovery periods. To improve the accuracy of human-computer interaction of ankle joint rehabilitation robots (AJRR), this study proposes a man-machine collaboration model of an EEG-driven AJRR. The model mainly expands from two levels (1) to establish the connection between EEG and intention so as to identify the intention. In the recognition process, first feature extraction is given on the preprocessed EEG. Convolutional neural network (CNN) is selected to extract the deep features of the EEG signal, and support vector machine (SVM) is used for classifying the deep features, thereby realizing intent recognition. (2) The result of intention recognition is input to the human-computer interaction (HCI) system, which controls the movement of the rehabilitation robot after receiving the instruction. This study truly realizes patient-oriented rehabilitation training. Experiments show that the human-machine collaboration model used can show higher accuracy of intention recognition, thereby increasing the satisfaction of using AJRR.

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“…It is used to enlarge the receptive field while maintaining the same number of parameters. Recently, many approaches focus on multimodal fusion and contextual information aggregation to improve semantic segmentations [52,54,55]. ParseNet [56] applies average pooling to the full image to capture the global contextual information.…”

Section: Semantic Segmentation With Cnnmentioning

confidence: 99%

PPANet: Point-Wise Pyramid Attention Network for Semantic Segmentation

Elhassan

Chen

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic segmentation. Specifically, the encoder adopts a novel squeeze nonbottleneck module as a base module to extract feature representations, where squeeze and expansion are utilized to obtain high segmentation accuracy. An upsampling module is designed to work as a decoder; its purpose is to recover the lost pixel-wise representations from the encoding part. The middle part consists of two parts point-wise pyramid attention (PPA) module and an attention-like module connected in parallel. The PPA module is proposed to utilize contextual information effectively. Furthermore, we developed a combined loss function from dice loss and binary cross-entropy to improve accuracy and get faster training convergence in KITTI road segmentation. The paper conducted the training and testing experiments on KITTI road segmentation and Camvid datasets, and the evaluation results show that the proposed method proved its effectiveness in road semantic segmentation.

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Alzheimer’s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion

Cited by 112 publications

References 30 publications

An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

PPANet: Point-Wise Pyramid Attention Network for Semantic Segmentation

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