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IEEE International Conference on Systems, Man and Cybernetics

作者： Neethu Robinson Seong-Whan Lee Cuntai Guan School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore Department of Brain and Cognitive Engineering Korea University Seoul South Korea

With deep learning emerging as a powerful machine learning tool to build brain computer Interface (BCI) systems, researchers are investigating the use of different type of networks architectures and representations of brain activity to attain superior classification accuracy compared to state-of-the-art machine learning approaches, that rely on processed signal and optimally extracted features. This paper presents a deep learning driven electroencephalography (EEG) -BCI system to perform decoding of hand motor imagery using deep convolution neural network architecture, with spectrally localized time-domain representation of multi-channel EEG as input. A significant increase in decoding performance in terms of accuracy of +6.47% is obtained compared to a wideband EEG representation. We further illustrate the movement class specific feature patterns for both the architectures and demonstrate that higher difference between classes is observed using the proposed architecture. We conclude that the network trained by taking into account the dynamic spatial interactions in distinct frequency bands of EEG, can offer better decoding performance and aid in better interpretation of learned features.

关键词： Electroencephalography Convolution Decoding computer architecture Deep learning Task analysis

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Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation

arXiv

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arXiv 2019年

作者： Liu, Yuzhou Wang, DeLiang Department of Computer Science and Engineering Ohio State University ColumbusOH43210-1277 United States Department of Computer Science and Engineering Center for Cognitive and Brain Sciences Ohio State University ColumbusOH43210-1277 United States

We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis (CASA). Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach optimizes frame-level separation and speaker tracking in turn, and produces excellent results for both objectives. Experimental results on the benchmark WSJ0-2mix database show that the new approach achieves the state-of-the-art results with a modest model size. Copyright © 2019, The Authors. All rights reserved.

关键词： Separation

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Predictive Human Emotion Recognition System using Deep Functional Affective State Modeling

Predictive Human Emotion Recognition System using Deep Funct...

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作者： Raja Majid Mehmood Hyung-Jeong Yang Sun-Hee Kim School of Electrical and Computer Engineering Information &Communication DepartmentXiamen University Malaysia Department of Computer Science Chonnam National University Department.of Brain & Cognitive Engineering Korea University

Emotions and humans are closely related to each other as emotion alleviate adaptive response to environmental changes and can act as a manner of communication about what is important to us. Emotions can be expressed through facial expressions, words, voice or speech articulation thus allowing us to conceive the emotional state of other individual and communicate with them in the best of our behavior. Emotion recognition is a process to classify different affective states of a human brain. It is a method through which we can analyze the emotive response to certain stimuli and develop human-computer interaction applications. Deep Learning algorithms recently gained attention for their accuracy, precision, speed and real time implementation. Emotion recognition has proved to be quite challenging because of its spectral-temporal pattern problems. In this study we propose a Deep Functional Affective State Model(DFASM) predictive model based on convolutional long short-term memory(Conv LSTM) using margin-based loss function. We evaluate the influence of eight emotional responses. The loss function used in this method observes more specific feelings during the training phase and allows the model to be more confident. The model is tested on public dataset(DEAP) and we recorded an increase up to 79% in the accuracy. Our proposed model is capable of capturing spatial-temporal data while learning, which helps in better emotional recognition. The proposed model was tested by using a public dataset(DEAP) and it outperformed other state-of-the-art methods.

关键词： EEG Emotions Recognition LSTM DEAP

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Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers

Npj imaging

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Npj imaging 2024年第1期2卷 56页

作者： Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E Black Alan R Moody April Khademi Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada. Institute for Biomedical Engineering Science Tech (iBEST) a Partnership between St. Michael's Hospital and Toronto Metropolitan University Toronto ON Canada. Keenan Research Center for Biomedical Science St. Michael's Hospital Unity Health Network Toronto ON Canada. Department of Medical Imaging University of Toronto Toronto ON Canada. Institute of Medical Science University of Toronto Toronto ON Canada. Horvitz Brain Sciences Research Program Sunnybrook Research Institute Toronto ON Canada. Division of Neurology Department of Medicine Sunnybrook Health Sciences Centre Toronto ON Canada. L.C. Campbell Cognitive Neurology Research Unit Sunnybrook Health Sciences Centre Toronto ON Canada. Vector Institute for Artificial Intelligence Toronto ON Canada.

This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly ( < 0.05) different across subgroups, while Fisher's tests revealed significant ( < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers.

关键词： brain imaging Image processing

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Overground gait patterns changed by modulating hip stiffness with a robotic exoskeleton*

Overground gait patterns changed by modulating hip stiffness...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Meghan E. Huber Jongwoo Lee Vibha Agarwal Haley Warren Neville Hogan Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Mechanical and Industrial Engineering University of Massachusetts Amherst Amherst MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biomedical Engineering University of Vermont Burlington VT USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728159072

ISBN: (纸本)9781728159089

Lower-limb exoskeletons have shown great potential to assist human locomotion. However, effective methods for modulating aspects of gait behavior beyond reducing metabolic effort, such as stride time and gait kinematics, are still needed, especially for individuals with impaired gait. Using the Samsung GEMS-H exoskeleton, we studied the effect on healthy gait behavior of applying hip stiffness during overground walking. We found that applying positive stiffness did not affect stride time, but it did reduce hip range of motion. We also found that applying negative stiffness increased both stride time and hip range of motion. Additional analyses showed that the effect of applied hip stiffness on hip range of motion during overground walking was similar to that observed during treadmill walking. The effect of positive stiffness on stride time was similar during overground and treadmill walking, but the increase in stride time in response to the application of negative stiffness was greater during treadmill walking than in overground walking. Lastly, we found no evidence to indicate that neural adaptation or learning occurred when hip stiffness was applied. These results suggest that applying joint stiffness may be a promising approach to restoring healthy gait kinematics.

关键词： Legged locomotion Hip Exoskeletons Robots Read only memory Time measurement Analysis of variance

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Push-pull feedback implements hierarchical information retrieval efficiently 33

Push-pull feedback implements hierarchical information retri...

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33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019

作者： Liu, Xiao Zou, Xiaolong Ji, Zilong Tian, Gengshuo Mi, Yuanyuan Huang, Tiejun Michael Wong, K.Y. Wu, Si School of Electronics Engineering and Computer Science IDG/McGovern Institute for Brain Research Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University China Department of Mathematics Beijing Normal University China Center for Neurointelligence Chongqing University China Department of Physics Hong Kong University of Science and Technology Hong Kong

Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a neural pathway. Although the importance of feedback in neural information processing has been widely recognized in the field, the detailed mechanism of how it works remains largely unknown. Here, we investigate the role of feedback in hierarchical information retrieval. Specifically, we consider a hierarchical network storing the hierarchical categorical information of objects, and information retrieval goes from rough to fine, aided by dynamical push-pull feedback from higher to lower layers. We elucidate that the push (positive) and pull (negative) feedbacks suppress the interferences due to neural correlations between different and the same categories, respectively, and their joint effect improves retrieval performance significantly. Our model agrees with the push-pull phenomenon observed in neural data and sheds light on our understanding of the role of feedback in neural information processing. © 2019 Neural information processing systems foundation. All rights reserved.

关键词： Feedback

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A Robust Matching Network for Gradually Estimating Geometric Transformation on Remote Sensing Imagery

A Robust Matching Network for Gradually Estimating Geometric...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Dong-Geon Kim Woo-Jeoung Nam Seong-Whan Lee Department of Brain and Cognitive Engineering Korea University Seongbuk-gu Seoul Republic of Korea Department of Computer Science and Engineering Korea University Seongbuk-gu Seoul Republic of Korea

In this paper, we propose a matching network for gradually estimating the geometric transformation parameters between two aerial images taken in the same area but in different environments. To precisely matching two aerial images, there are important factors to consider such as different time, a variation of viewpoint, size, and rotation. The conventional methods for matching aerial image pairs with the large variations are extremely time-consuming process and have the limitations finding correct correspondences, because the image gradient and grayscale intensity for generating the feature descriptors are not robust to the variations. We design the network architecture as an end-to-end trainable deep neural network to reflect the characteristics of aerial images. The hierarchical structures that orderly estimate the rotation and the affine transformations make it possible to reduce the range of predictions and minimize errors caused by misalignment, resulting in more precise matching performance. Furthermore, we apply transfer learning to make the feature extraction networks more robust and suitable for the aerial image domain with the large variations. For the experiment, we apply the remote sensing image datasets from Google Earth and International Society for Photogrammetry and Remote Sensing (ISPRS). To evaluate our method quantitatively, we measure the probability of correct keypoints (PCK) metrics for objectively comparing the degree of matching. In terms of qualitative and quantitative assessment, our method demonstrates the state-of-the-art performances compared to the existing methods.

关键词： Feature extraction Correlation Image matching Training Meteorology Semantics Remote sensing

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GLEU-guided multi-resolution network for short text conversation 1

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14th National Conference on Man-Machine Speech Communication, NCMMSC 2017

作者： Liu, Xuan Yu, Kai Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789811081118

ISBN: (纸本)9789811081101

With the recent development of sequence-to-sequence framework, generation approach for short text conversation becomes attractive. Traditional sequence-to-sequence method for short text conversation often suffers from dull response problem. Multi-resolution generation approach has been introduced to address this problem by dividing the generation process into two steps: keywords-sequence generation and response generation. However, this method still tends to generate short and dull keywords-sequence. In this work, a new multi-resolution generation framework is proposed. Instead of using the word-level maximum likelihood criterion, we optimize the sequence-level GLEU score of the entire generated keywords-sequence using a policy gradient approach in reinforcement learning. Experiments show that the proposed approach can generate longer and more diverse keywords-sequence. Meanwhile, it achieves better scores in the human evaluation. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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All-neural multi-channel speech enhancement 19

All-neural multi-channel speech enhancement

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19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Wang, Zhong-Qiu Wang, DeLiang Department of Computer Science and Engineering Ohio State University United States Center for Cognitive and Brain Sciences Ohio State University United States

This study proposes a novel all-neural approach for multichannel speech enhancement, where robust speaker localization, acoustic beamforming, post-filtering and spatial filtering are all done using deep learning based time-frequency (T-F) masking. Our system first performs monaural speech enhancement on each microphone signal to obtain the estimated ideal ratio masks for beamforming and robust time delay of arrival (TDOA) estimation. Then with the estimated TDOA, directional features indicating whether each T-F unit is dominated by the signal coming from the estimated target direction are computed. Next, the directional features are combined with the spectral features extracted from the beamformed signal to achieve further enhancement. Experiments on a two-microphone setup in reverberant environments with strong diffuse babble noise demonstrate the effectiveness of the proposed approach for multi-channel speech enhancement. © 2018 International Speech Communication Association. All rights reserved.

关键词： Beamforming

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High-quality voice conversion using spectrogram-based WaveNet Vocoder 19

High-quality voice conversion using spectrogram-based WaveNe...

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19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Chen, Kuan Chen, Bo Lai, Jiahao Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Waveform generator is a key component in voice conversion. Recently, WaveNet waveform generator conditioned on the Mel-cepstrum (Mcep) has shown better quality over standard vocoder. In this paper, an enhanced WaveNet model based on spectrogram is proposed to further improve voice conversion performance. Here, Mel-frequency spectrogram is converted from source speaker to target speaker using an LSTM-RNN based frame-to-frame feature mapping. To evaluate the performance, the proposed approach is compared to an Mcep based LSTM-RNN voice conversion system. Both STRAIGHT vocoder and Mcep-based WaveNet vocoder are elected to produce the converted speech for Mcep conversion system. The fundamental frequency (F0) of the converted speech in different systems is analyzed. The naturalness, similarity and intelligibility are evaluated in subjective measures. Results show that the spectrogram based WaveNet waveform generator can achieve better voice conversion quality compared to traditional WaveNet approaches. The Mel-spectrogram based voice conversion can achieve significant improvement in speaker similarity and inherent F0 conversion. © 2018 International Speech Communication Association. All rights reserved.

关键词： Vocoders

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