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  Subjects -> ELECTRONICS (Total: 207 journals)
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IEEE Transactions on Autonomous Mental Development
Number of Followers: 8  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1943-0604
Published by IEEE Homepage  [228 journals]
  • Local Multimodal Serial Analysis for Fusing EEG-fMRI: A New Method to
           Study Familial Cortical Myoclonic Tremor and Epilepsy

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      Authors: Li Dong;Pu Wang;Yi Bin;Jiayan Deng;Yongjie Li;Leiting Chen;Cheng Luo;Dezhong Yao;
      Pages: 311 - 319
      Abstract: Integrating information of neuroimaging multimodalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), has become popularly for investigating various types of epilepsy. However, there are also some problems for the analysis of simultaneous EEG-fMRI data in epilepsy: one is the variation of HRFs, and another is low signal-to-noise ratio (SNR) in the data. Here, we propose a new multimodal unsupervised method, termed local multimodal serial analysis (LMSA), which may compensate for these deficiencies in multimodal integration. A simulation study with comparison to the traditional EEG-informed fMRI analysis which directly implemented the general linear model (GLM) was conducted to confirm the superior performance of LMSA. Then, applied to the simultaneous EEG-fMRI data of familial cortical myoclonic tremor and epilepsy (FCMTE), some meaningful information of BOLD changes related to the EEG discharges, such as the cerebellum and frontal lobe (especially in the inferior frontal gyrus), were found using LMSA. These results demonstrate that LMSA is a promising technique for exploring various data to provide integrated information that will further our understanding of brain dysfunction.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Discriminating Bipolar Disorder From Major Depression Based on SVM-FoBa:
           Efficient Feature Selection With Multimodal Brain Imaging Data

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      Authors: Nan-Feng Jie;Mao-Hu Zhu;Xiao-Ying Ma;Elizabeth A Osuch;Michael Wammes;Jean Théberge;Huan-Dong Li;Yu Zhang;Tian-Zi Jiang;Jing Sui;Vince D Calhoun;
      Pages: 320 - 331
      Abstract: Discriminating between bipolar disorder (BD) and major depressive disorder (MDD) is a major clinical challenge due to the absence of known biomarkers; hence a better understanding of their pathophysiology and brain alterations is urgently needed. Given the complexity, feature selection is especially important in neuroimaging applications, however, feature dimension and model understanding present serious challenges. In this study, a novel feature selection approach based on linear support vector machine with a forward-backward search strategy (SVM-FoBa) was developed and applied to structural and resting-state functional magnetic resonance imaging data collected from 21 BD, 25 MDD and 23 healthy controls. Discriminative features were drawn from both data modalities, with which the classification of BD and MDD achieved an accuracy of 92.1% (1000 bootstrap resamples). Weight analysis of the selected features further revealed that the inferior frontal gyrus may characterize a central role in BD-MDD differentiation, in addition to the default mode network and the cerebellum. A modality-wise comparison also suggested that functional information outweighs anatomical by a large margin when classifying the two clinical disorders. This work validated the advantages of multimodal joint analysis and the effectiveness of SVM-FoBa, which has potential for use in identifying possible biomarkers for several mental disorders.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • Design of a Multimodal EEG-based Hybrid BCI System with Visual Servo
           Module

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      Authors: Feng Duan;Dongxue Lin;Wenyu Li;Zhao Zhang;
      Pages: 332 - 341
      Abstract: Current EEG-based brain-computer interface technologies mainly focus on how to independently use SSVEP, motor imagery, P300, or other signals to recognize human intention and generate several control commands. SSVEP and P300 require external stimulus, while motor imagery does not require it. However, the generated control commands of these methods are limited and cannot control a robot to provide satisfactory service to the user. Taking advantage of both SSVEP and motor imagery, this paper aims to design a hybrid BCI system that can provide multimodal BCI control commands to the robot. In this hybrid BCI system, three SSVEP signals are used to control the robot to move forward, turn left, and turn right; one motor imagery signal is used to control the robot to execute the grasp motion. In order to enhance the performance of the hybrid BCI system, a visual servo module is also developed to control the robot to execute the grasp task. The effect of the entire system is verified in a simulation platform and a real humanoid robot, respectively. The experimental results show that all of the subjects were able to successfully use this hybrid BCI system with relative ease.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • EEG-Based Perceived Tactile Location Prediction

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      Authors: Deng Wang;Yadong Liu;Dewen Hu;Gunnar Blohm;
      Pages: 342 - 348
      Abstract: Previous studies have attempted to investigate the peripheral neural mechanisms implicated in tactile perception, but the neurophysiological data in humans involved in tactile spatial location perception to help the brain orient the body and interact with its surroundings are not well understood. In this paper, we use single-trial electroencephalogram (EEG) measurements to explore the perception of tactile stimuli located on participants' right forearm, which were approximately equally spaced centered on the body midline, 2 leftward and 2 rightward of midline. An EEG-based signal analysis approach to predict the location of the tactile stimuli is proposed. Offline classification suggests that tactile location can be detected from EEG signals in single trial (four-class classifier for location discriminate can achieve up to 96.76%) with a short response time (600 milliseconds after stimulus presentation). From a human-machine-interaction (HMI) point of view, this could be used to design a real-time reactive control machine for patients, e.g., suffering from hypoesthesia.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
  • An Adaptive Motion-Onset VEP-Based Brain-Computer Interface

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      Authors: Rui Zhang;Peng Xu;Rui Chen;Teng Ma;Xulin Lv;Fali Li;Peiyang Li;Tiejun Liu;Dezhong Yao;
      Pages: 349 - 356
      Abstract: Motion-onset visual evoked potential (mVEP) has been recently proposed for EEG-based brain-computer interface (BCI) system. It is a scalp potential of visual motion response, and typically composed of three components: P1, N2, and P2. Usually several repetitions are needed to increase the signal-to-noise ratio (SNR) of mVEP, but more repetitions will cost more time thus lower the efficiency. Considering the fluctuation of subject's state across time, the adaptive repetitions based on the subject's real-time signal quality is important for increasing the communication efficiency of mVEP-based BCI. In this paper, the amplitudes of the three components of mVEP are proposed to build a dynamic stopping criteria according to the practical information transfer rate (PITR) from the training data. During online test, the repeated stimulus stopped once the predefined threshold was exceeded by the real-time signals and then another circle of stimulus newly began. Evaluation tests showed that the proposed dynamic stopping strategy could significantly improve the communication efficiency of mVEP-based BCI that the average PITR increases from 14.5 bit/min of the traditional fixed repetition method to 20.8 bit/min. The improvement has great value in real-life BCI applications because the communication efficiency is very important.
      PubDate: Dec. 2015
      Issue No: Vol. 7, No. 4 (2015)
       
 
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