MÜLLER N , REERMANN J , MEISEN T . Navigating the depths: a comprehensive survey of deep learning for passive underwater acoustic target recognition [J ] . IEEE Access , 2024 , 12 : 154092 - 154118 .

DOAN V S , HUYNH-THE T , KIM D S . Underwater acoustic target classification based on dense convolutional neural network [J ] . IEEE Geoscience and Remote Sensing Letters , 2022 , 19 : 1 - 5 .

LEI F , TANG F F , LI S H . Underwater target detection algorithm based on improved YOLOv5 [J ] . Journal of Marine Science and Engineering , 2022 , 10 ( 3 ): 310 .

XU G F , ZHOU D X , YUAN L B , et al. Vision-based underwater target real-time detection for autonomous underwater vehicle subsea exploration [J ] . Frontiers in Marine Science , 2023 , 10 : 1112310 .

薛灵芝 , 曾向阳 , 杨爽 . 基于生成对抗网络的水声目标识别算法 [J ] . 兵工学报 , 2021 , 42 ( 11 ): 2444 - 2452 . DOI: 10.3969/j.issn.1000-1093.2021.11.018 http://doi.org/10.3969/j.issn.1000-1093.2021.11.018 目标识别是水声探测领域的难题，也是研究热点。在水声目标识别实际应用中，标记样本数量不足是制约识别结果的主要因素之一。针对水声目标噪声数据具有的小样本特点，基于深度学习理论提出一种基于生成对抗网络的识别模型。该模型从生成模型与对抗模型的相互博弈中，学习更多有效的识别特征信息，并与深度自编码网络和深度置信网络模型进行对比。仿真实验结果表明：在样本数量有限的情况下，生成对抗网络模型的识别效果优于深度置信网络与深度自编码网络；3种深度学习模型的识别性能均优于先提取梅尔倒谱系数特征，再用Softmax分类的方法。为进一步测试所建模型的性能，研究了3种深度学习模型在不同信噪比下的鲁棒性，仿真实验结果表明：生成对抗网络模型对噪声具有更强的鲁棒性。

XUE L Z , ZENG X Y , YANG S . Underwater acoustic target recognition algorithm based on generative adversarial networks [J ] . Acta Armamentarii , 2021 , 42 ( 11 ): 2444 - 2452 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.11.018 http://doi.org/10.3969/j.issn.1000-1093.2021.11.018 In the practical application of underwater acoustic target recognition，one of the main factors restricting the recognition results is the insufficient quantity of labeled samples. For the small sample properties of underwater acoustic target noise，a generative adversarial networks(GAN)-based recognition algorithm is proposed based on deep learning theory. It can be used to learn more effective features with more discriminative information from the game between generated model and adversarial model，and it is compared with deep auto-encoder(DAE) network and deep belief network(DBN) models. The experimental results illustrate that the recognition performance of GAN network model is higher than those of DBN network and DAE network models when the number of samples is limited，and the recognition performances of the three deep learning models are better than the conventional approach of extracting Mel frequency cepstrum coefficient(MFCC) features and then classifying by Softmax. In addition，GAN network model is superior to DBN network and DAE network models in recognition rate when using training samples and test samples with different SNRs. The smulation experimental results indicate that the GAN network model is more robust to noise.

MENG Q X , YANG S E , PIAO S C . The classification of underwater acoustic target signals based on wave structure and support vector machine [J ] . Journal of the Acoustical Society of America , 2014 , 136 ( 4 ): 2265 .

MOURA N N D , SEIXAS J M D . Novelty detection in passive SONAR systems using support vector machines [C ] //Proceedings of 2015 Latin America Congress on Computational Intelligence.Washington,D.C., US:IEEE , 2015 .

DAVID S D , SOLEDAD T G , ANTONIO C L , et al. ShipsEar: an underwater vessel noise database [J ] . Applied Acoustics , 2016 , 113 : 64 - 69 .

LUO X W , CHEN L , ZHOU H L , et al. A survey of underwater acoustic target recognition methods based on machine learning [J ] . Journal of Marine Science and Engineering , 2023 , 11 ( 2 ): 384 .

DOAN S V , HUYNH-THE T , KIM D S . Underwater acoustic target classification based on dense convolutional neural network [J ] . IEEE Geoscience and Remote Sensing Letters , 2020 , 99 : 1 - 5 .

LI J F , ZHAO G F , LI B B , et al. A reduced dimension multiple signal classification-based direct location algorithm with dense arrays [J ] . International Journal of Distributed Sensor Networks , 2022 , 18 ( 5 ): 7153 - 7160 .

TIAN S Z , BAI D , ZHOU J L , et al. Few-shot learning for joint model in underwater acoustic target recognition [J ] . Scientific Reports , 2023 , 13 ( 1 ): 17502 .

IRFAN M , JIANGBIN Z , ALI S , et al. DeepShip: an underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification [J ] . Expert Systems with Applications , 2021 , 183 ( 5 ): 115270 .

XIE Y , REN J W , XU J . Guiding the underwater acoustic target recognition with interpretable contrastive learning [C ] // Proceedings of the OCEANS 2023.Limerick , Ireland : IEEE , 2023 : 1 - 6 .

徐宝国 , 何小杭 , 魏智唯 , 等 . 基于运动想象脑电的机器人连续控制系统研究 [J ] . 仪器仪表学报 , 2018 , 39 ( 9 ): 10 - 19 .

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VAUGHAN T M , HEETDERKS W J , TREJO L J , et al. Brain-computer interface technology:a review of the Second International Meeting [J ] . IEEE Transactions on Neural Systems & Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society , 2003 , 11 ( 2 ): 94 - 109 .

NAKANISHI M , WANG Y J , CHEN X G , et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis [J ] . IEEE Transactions on Biomedical Engineering , 2017 , 65 ( 1 ): 104 - 112 .

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GRAMANN K , GWIN J T , BIGDELY-SHAMLO N , et al. Visual evoked responses during standing and walking [J ] . Frontiers in Human Neuroscience , 2010 , 4 : 202 . DOI: 10.3389/fnhum.2010.00202 http://doi.org/10.3389/fnhum.2010.00202 Human cognition has been shaped both by our body structure and by its complex interactions with its environment. Our cognition is thus inextricably linked to our own and others' motor behavior. To model brain activity associated with natural cognition, we propose recording the concurrent brain dynamics and body movements of human subjects performing normal actions. Here we tested the feasibility of such a mobile brain/body (MoBI) imaging approach by recording high-density electroencephalographic (EEG) activity and body movements of subjects standing or walking on a treadmill while performing a visual oddball response task. Independent component analysis of the EEG data revealed visual event-related potentials that during standing, slow walking, and fast walking did not differ across movement conditions, demonstrating the viability of recording brain activity accompanying cognitive processes during whole body movement. Non-invasive and relatively low-cost MoBI studies of normal, motivated actions might improve understanding of interactions between brain and body dynamics leading to more complete biological models of cognition.

WANG R D , LIU Y , SHI J T , et al. Sound target detection under noisy environment using brain-computer interface [J ] . IEEE Transactions on Neural Systems and Rehabilitation Engineering , 2023 , 31 : 229 - 237 .

HAN X K , NIU J Y , GUO S J . A tactile-based brain computer interface P300 paradigm using vibration frequency and spatial location [J ] . Journal of Medical and Biological Engineering , 2020 , 40 : 773 - 782 .

KOJIMA S , KANOH S I . An auditory brain-computer interface based on selective attention to multiple tone streams [J ] . Plos One , 2024 , 19 ( 5 ): e0303565 .

HUANG W C , ZHANG P Q , YU T Y , et al. A P300-based BCI system using stereoelectroencephalography and its application in a brain mechanistic study [J ] . IEEE Transactions on Biomedical Engineering , 2020 , 68 ( 8 ): 2509 - 2519 .

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GRANGER C W . Investigating causal relations by econometric models and cross-spectral methods [J ] . Econometrica: Journal of the Econometric Society , 1969 , 37 ( 3 ): 424 - 438 .

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SCHREIBER T . Measuring information transfer [J ] . Physical Review Letters , 2000 , 85 ( 2 ): 461 - 464 . An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.

PALVA J M , PALVA S , KAILA K . Phase synchrony among neuronal oscillations in the human cortex [J ] . Journal of Neuroscience , 2005 , 25 ( 15 ): 3962 - 3972 . DOI: 10.1523/JNEUROSCI.4250-04.2005 http://doi.org/10.1523/JNEUROSCI.4250-04.2005 Synchronization of neuronal activity, often associated with network oscillations, is thought to provide a means for integrating anatomically distributed processing in the brain. Neuronal processing, however, involves simultaneous oscillations in various frequency bands. The mechanisms involved in the integration of such spectrally distributed processing have remained enigmatic. We demonstrate, using magnetoencephalography, that robust cross-frequency phase synchrony is present in the human cortex among oscillations with frequencies from 3 to 80 Hz. Continuous mental arithmetic tasks demanding the retention and summation of items in the working memory enhanced the cross-frequency phase synchrony among alpha (approximately 10 Hz), beta (approximately 20 Hz), and gamma (approximately 30-40 Hz) oscillations. These tasks also enhanced the "classical" within-frequency synchrony in these frequency bands, but the spatial patterns of alpha, beta, and gamma synchronies were distinct and, furthermore, separate from the patterns of cross-frequency phase synchrony. Interestingly, an increase in task load resulted in an enhancement of phase synchrony that was most prominent between gamma- and alpha-band oscillations. These data indicate that cross-frequency phase synchrony is a salient characteristic of ongoing activity in the human cortex and that it is modulated by cognitive task demands. The enhancement of cross-frequency phase synchrony among functionally and spatially distinct networks during mental arithmetic tasks posits it as a candidate mechanism for the integration of spectrally distributed processing.

YUAN J J , LUO Y J , YAN J H , et al. Neural correlates of the females’ susceptibility to negative emotions: an insight into gender-related prevalence of affective disturbances [J ] . Human Brain Mapping , 2009 , 30 ( 11 ): 3676 - 3686 .

CHEN Y W , LIN C J . Combining SVMs with various feature selection strategies [J ] . Studies in Fuzziness & Soft Computing , 2008 , 207 : 315 - 324 .

XIE J Y , WANG C X , JIANG S , et al. Feature selection method combing improved F-score and support vector machine [J ] . Journal of Computer Applications , 2010 , 30 ( 4 ): 993 - 996 .

DELORME A , MAKEIG S . EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis [J ] . Journal of Neuroscience Methods , 2004 , 134 ( 1 ): 9 - 21 . DOI: 10.1016/j.jneumeth.2003.10.009 http://doi.org/10.1016/j.jneumeth.2003.10.009 We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.

MOGNON A , JOVICICH J , BRUZZONE L , et al. ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features [J ] . Psychophysiology , 2011 , 48 ( 2 ): 229 - 240 . DOI: 10.1111/j.1469-8986.2010.01061.x http://doi.org/10.1111/j.1469-8986.2010.01061.x A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Copyright © 2010 Society for Psychophysiological Research.

KARTON I , BACHMANN T . Disrupting dorsolateral prefrontal cortex by rTMS reduces the P300 based marker of deception [J ] . Brain and Behavior , 2017 , 7 ( 4 ): e00656 .

MOLHOLM S , SEHATPOUR P , MEHTA A D , et al. Audio-visual multisensory integration in superior parietal lobule revealed by human intracranial recordings [J ] . Journal of Neurophysiology , 2006 , 96 ( 2 ): 721 - 729 . DOI: 10.1152/jn.00285.2006 http://doi.org/10.1152/jn.00285.2006 Intracranial recordings from three human subjects provide the first direct electrophysiological evidence for audio-visual multisensory processing in the human superior parietal lobule (SPL). Auditory and visual sensory inputs project to the same highly localized region of the parietal cortex with auditory inputs arriving considerably earlier (30 ms) than visual inputs (75 ms). Multisensory integration processes in this region were assessed by comparing the response to simultaneous audio-visual stimulation with the algebraic sum of responses to the constituent auditory and visual unisensory stimulus conditions. Significant integration effects were seen with almost identical morphology across the three subjects, beginning between 120 and 160 ms. These results are discussed in the context of the role of SPL in supramodal spatial attention and sensory-motor transformations.