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001453459 0247_ $$a10.1007/978-981-19-8222-4$$2doi
001453459 035__ $$aSP(OCoLC)1352975841
001453459 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dOCLCQ
001453459 049__ $$aISEA
001453459 050_4 $$aQA76.87
001453459 08204 $$a006.3$$223/eng/20221212
001453459 1112_ $$aInternational Workshop on Human Brain and Artificial Intelligence$$n(3rd :$$d2022 :$$cVienna, Austria)
001453459 24510 $$aHuman brain and artificial intelligence :$$bthird International Workshop, HBAI 2022, held in conjunction with IJCAI-ECAI 2022,Vienna, Austria, July 23, 2022, Revised Selected Papers /$$cXiaomin Ying (ed.).
001453459 2463_ $$aHBAI 2022
001453459 260__ $$aSingapore :$$bSpringer,$$c2023.
001453459 300__ $$a1 online resource (237 p.).
001453459 4901_ $$aCommunications in Computer and Information Science ;$$v1692
001453459 500__ $$a2.4 Deep Residual Learning Framework
001453459 5050_ $$aIntro -- Preface -- Organization -- Contents -- AI for Brain Related Data Analysis -- Classification of EEG Signals Based on GA-ELM Optimization Algorithm -- 1 Introduction -- 2 Optimization of Extreme Learning Machine by Genetic Algorithm -- 3 The Experiment Design -- 3.1 Experimental System Framework -- 3.2 Data Acquisition -- 4 The Data Analysis -- 4.1 Preprocessing -- 4.2 Feature Extraction -- 4.3 Genetic Algorithm Optimized Parameter Setting -- 5 Results -- 6 Discussion -- 7 Conclusion -- References
001453459 5058_ $$aDelving into Temporal-Spectral Connections in Spike-LFP Decoding by Transformer Networks -- 1 Introduction -- 2 Methods -- 2.1 Temporal Connection Learning with Spikes -- 2.2 Spectral Connection Learning with LFPs -- 2.3 Temporal-Spectral Connection Learning with Spike-LFPs -- 2.4 Task-Related Output Layer -- 3 Experiments and Results -- 3.1 Clinical Dataset -- 3.2 Spike-LFP Fusion Improves Neural Decoding Accuracy -- 3.3 Temporal Connections Improve Robustness to Temporal Shifts -- 3.4 Temporal-Spectral Connections Improve Robustness to Noises -- 4 Conclusion
001453459 5058_ $$aA Detail Settings Of Neural Decoders -- B Estimating Movement Conduction Durations With Neuron Responses -- C Robustness To Gaussian Noises -- References -- A Mask Image Recognition Attention Network Supervised by Eye Movement -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 The Generation of Gaze Heat Map -- 2.3 Network Architecture -- 3 Results -- 3.1 Eye Movement Heat Map -- 3.2 Network Performance -- 3.3 Network Attention Visualization -- 4 Conclusion -- References -- DFC-SNN: A New Approach for the Recognition of Brain States by Fusing Brain Dynamics and Spiking Neural Network
001453459 5058_ $$a1 Introduction -- 2 Methods -- 2.1 DFC-SNN Framework -- 2.2 Dataset -- 3 Results -- 4 Conclusion -- References -- DSNet: EEG-Based Spatial Convolutional Neural Network for Detecting Major Depressive Disorder -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset and Data Preprocessing -- 2.2 The Architecture of DSNet -- 2.3 Baseline Methods -- 2.4 Model Implementation and Experimental Evaluation -- 3 Results and Discuss -- 4 Conclusion -- References -- SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder
001453459 5058_ $$a1 Introduction -- 2 Materials and Methods -- 2.1 Data and Preprocessing -- 2.2 1DCNN and LSTM Network -- 2.3 Channel Attention -- 2.4 Evaluation Metrics and Parameters -- 3 Results and Discussion -- 3.1 Comparison with Baseline Method -- 3.2 Ablation Study -- 3.3 Interpretability Analysis of Channel Attention -- 3.4 Effects of Window Size -- 4 Conclusion -- References -- Emotion Recognition from EEG Using All-Convolution Residual Neural Network -- 1 Introduction -- 2 Methods -- 2.1 Pre-processing and Feature Extraction -- 2.2 3D Input Construction -- 2.3 The All-Convolutional Neural Network
001453459 506__ $$aAccess limited to authorized users.
001453459 520__ $$aThis book constitutes the refereed proceedings of the Third International Workshop on Human Brain and Artificial Intelligence, HBAI 2022, held in conjunction with IJCAI-ECAI 2022, Vienna, Austria, on July 23, 2022. The 19 full papers presented were carefully reviewed and selected from 21 submissions. The papers present most recent research in the fields of brain-inspired computing, brain-machine interfaces, computational neuroscience, brain-related health, neuroimaging, cognition and behavior, learning, and memory, neuron modulation, and closed-loop brain stimulation.
001453459 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 12, 2022).
001453459 650_0 $$aNeural computers$$vCongresses.
001453459 650_0 $$aBrain-computer interfaces$$vCongresses.
001453459 650_0 $$aComputational neuroscience$$vCongresses.
001453459 650_0 $$aArtificial intelligence$$vCongresses.
001453459 655_0 $$aElectronic books.
001453459 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001453459 7001_ $$aYing, Xiaomin,$$eeditor.
001453459 7112_ $$aInternational Joint Conference on Artificial Intelligence$$n(31st :$$d2022 :$$cVienna, Austria)
001453459 7112_ $$aEuropean Conference on Artificial Intelligence$$n(23rd :$$d2022 :$$cVienna, Austria)
001453459 77608 $$iPrint version:$$aYing, Xiaomin$$tHuman Brain and Artificial Intelligence$$dSingapore : Springer,c2023$$z9789811982217
001453459 830_0 $$aCommunications in computer and information science ;$$v1692.
001453459 852__ $$bebk
001453459 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-8222-4$$zOnline Access$$91397441.1
001453459 909CO $$ooai:library.usi.edu:1453459$$pGLOBAL_SET
001453459 980__ $$aBIB
001453459 980__ $$aEBOOK
001453459 982__ $$aEbook
001453459 983__ $$aOnline
001453459 994__ $$a92$$bISE