{"id":32,"date":"2026-02-21T18:29:23","date_gmt":"2026-02-21T10:29:23","guid":{"rendered":"https:\/\/sioklab.com\/?page_id=32"},"modified":"2026-05-31T09:22:27","modified_gmt":"2026-05-31T01:22:27","slug":"news","status":"publish","type":"page","link":"https:\/\/sioklab.com\/index.php\/news\/","title":{"rendered":"News"},"content":{"rendered":"\t\t
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News<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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[06\/2026] Dr. Nizhuan Wang Invited to Deliver a Talk at The Affiliated Suzhou Hospital of Nanjing University Medical School!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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We are delighted to share that Dr. Nizhuan Wang has been invited to deliver a presentation at The Affiliated Suzhou Hospital of Nanjing University Medical School on \u201cFrom Neuroimaging to Brain\u2013Computer Interfaces: The Advanced AI-Powered Brain Decoding Revolution.\u201d<\/strong><\/p>

This presentation will focus on the rapidly developing field of AI-powered brain decoding. Dr. Wang will discuss AI-powered neuroimaging computation, AI-powered brain\u2013computer interfaces, and the broader applications of artificial intelligence in brain decoding and neuroscience research<\/strong>. The talk will highlight how advanced AI methods are transforming the interpretation of brain activity and opening new possibilities for neuroimaging analysis, brain\u2013computer interfaces, computational linguistics, and neurolinguistics.<\/p>

Dr. Wang is a Principal Investigator and Research Assistant Professor in the Department of Language Science and Technology at The Hong Kong Polytechnic University. His main research interests include artificial intelligence and its interdisciplinary applications, brain\u2013computer interfaces, computational linguistics, neuroimaging, and neurolinguistics<\/strong>. He has published more than 90 papers in leading journals and conferences, including IEEE TMI, IEEE JBHI, Neurocomputing, Human Brain Mapping, AAAI, and MICCAI<\/strong>.<\/p>

The talk will take place at 16:00 on June 8, 2026<\/strong>, in the Conference Room on the 4th Floor of the Administration Building<\/strong>. More information about Dr. Wang is available at:
https:\/\/www.polyu.edu.hk\/lst\/people\/academic-staff\/dr-wang-nizhuan\/<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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[06\/2026] Dr. Nizhuan Wang Invited to Serve as Session Chair at the 2026 Shanghai Brain Science Theory Seminar<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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We are pleased to share that Dr. Nizhuan Wang<\/strong> has been invited to serve as a Session Chair<\/strong>\u00a0at the 2026 Shanghai Brain Science Theory Seminar<\/strong>, to be held on June 5\u20137, 2026<\/strong>, in Shanghai, China<\/strong>.<\/p>

Hosted by the Editorial Office of Cognitive Neurodynamics<\/strong>, the seminar will focus on key scientific questions in brain theory, neural dynamics, brain-inspired intelligence, neuroengineering, and AI applications<\/strong>.<\/p>

As Session Chair, Dr. Wang will help facilitate academic discussions and exchanges among leading scholars in brain science and intelligent systems.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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[05\/2026] Congratulations to Haodong\u2019s paper is accepted by Sensors!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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Haodong Zhang, Wai Ting Siok, Nizhuan Wang<\/b>. Imagined Speech Brain\u2013Computer Interface: A Task-Oriented Review of Neural Decoding [J]. Sensors, 2026.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Abstract<\/strong>:<\/p>

Imagined speech decoding has attracted growing interest in brain\u2013computer interface (BCI) research, as it may enable language-related information to be recovered from non-overt neural activity. Current studies in this area are often treated as a single, unified research problem, despite substantial differences in decoding target, output constraints, and system output forms. This review examines recent imagined speech decoding research from a task-oriented perspective, with a focus on how different neural decoding tasks are defined, constrained by their output spaces, and expressed through different output pathways. The included studies are organized into four main task levels: semantic\/intent, phoneme\/syllable, word, and sentence\/language decoding. They are further compared along two auxiliary dimensions: output-space property and output pathway, with particular attention to closed-set and open-vocabulary settings. The review shows that current studies span markedly different linguistic granularities and communication objectives, from low-bandwidth intent recognition to text or speech reconstruction. Finally, it concludes that imagined speech should not be treated as a single homogeneous decoding problem, and that a task-oriented framework provides a clearer basis for comparing heterogeneous studies and guiding future communication-oriented BCI research.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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[05\/2026] Dr. Nizhuan Wang Invited to Deliver a Talk at Translational-Neuro.org !<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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We are delighted to share that Dr. Nizhuan Wang has been invited to deliver an online presentation at Translational-Neuro.org on \u201cFrom Neuroimaging to Brain\u2013Computer Interfaces: The Advanced AI-Powered Brain Decoding Revolution\u201d.<\/p>

This presentation will focus on the rapidly advancing field of AI-powered brain decoding. Dr. Wang will discuss AI-powered neuroimaging computation<\/strong>, AI-powered brain\u2013computer interfaces<\/strong>, and future perspectives in AI, neuroimaging, BCI, and brain disorders<\/strong>. The talk will highlight how advanced artificial intelligence approaches are transforming the interpretation of brain activity and opening new possibilities for neuroimaging analysis, brain\u2013computer interfaces, and translational neuroscience research.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Read the full event details here: Translational-Neuro.org<\/em>
https:\/\/www.translational-neuro.org\/event-details\/from-neuroimaging-to-brain-computer-interfaces-the-advanced-ai-powered-brain-decoding-revolution<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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[04\/2026] Congratulations to Kang\u2019s paper is accepted by Neural Networks!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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Zijian Kang*, Yueyang Li*, Shengyu Gong, Weiming Zeng, Hongjie Yan, Lingbin Bian, Zhiguo Zhang, Wai Ting Siok, Nizhuan Wang<\/strong>. Hypergraph multi-modal learning for eeg-based emotion recognition in conversation [J]. Neural Networks, 2026<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Abstract:<\/strong><\/p>

Emotional Recognition in Conversation (ERC) is valuable for diagnosing health conditions such as autism and depression, and for understanding the emotions of individuals who struggle to express their feelings. Current ERC methods primarily rely on semantic, audio and visual data but face significant challenges in integrating physiological signals such as Electroencephalography (EEG). This research proposes Hypergraph Multi-Modal Learning (Hyper-MML), a novel framework for identifying emotions in conversation. Hyper-MML effectively integrates EEG with audio and video information to capture complex emotional dynamics. Firstly, we introduce an Adaptive Brain Encoder with Mutual-cross Attention (ABEMA) module for processing EEG signals. This module captures emotion-relevant features across different frequency bands and adapts to subject-specific variations through hierarchical mutual-cross attention mechanisms. Secondly, we propose an Adaptive Hypergraph Fusion Module (AHFM) to actively model the higher-order relationships among multi-modal signals in ERC. Experimental results on the EAV and AFFEC datasets demonstrate that our Hyper-MML model significantly outperforms current state-of-the-art methods. The proposed Hyper-MML can serve as an effective communication tool for healthcare professionals, enabling better engagement with patients who have difficulty expressing their emotions. The official implementation codes are available at https:\/\/github.com\/NZWANG\/Hyper-MML.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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[04\/2026] LST Research Seminar: Prof. GUO Yi to Give a Talk at PolyU (29 April 2026)!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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We are pleased to share that the Department of Language Science and Technology at The Hong Kong Polytechnic University will host Prof. Yi Guo from Southern University of Science and Technology for an LST Research Seminar on 29 April 2026 (Wednesday).<\/p>

In this talk, titled \u201cEEG-Based Brain Function Assessment and Precision Neuromodulation,\u201d Prof. Guo will present how high-density EEG can be transformed from a routine recording tool into an actionable platform for functional brain assessment, cognitive risk stratification, and personalized neuromodulation. Using the development of the Electroencephalography-based Cognitive Decline Screening System (E-CDS) as an example, he will demonstrate how spectral, connectivity, and network-level EEG markers can support the early detection of cognitive decline, differential assessment across neuropsychiatric disorders, and monitoring of treatment response.<\/p>

The talk will also discuss the closed-loop integration of EEG and transcranial magnetic stimulation (TMS) for precision intervention in cognitive and affective disorders, highlighting the potential of advanced neurotechnology in both research and clinical applications.<\/p>

Prof. Yi Guo, MD, PhD, is Chief Brain Health Expert at United Family Healthcare, Director of the Functional Neuroscience Center at Tianjin Huanhu Hospital, and Professor and Doctoral Supervisor at Southern University of Science and Technology. He is a senior neurologist specializing in cerebrovascular disease, cognitive impairment, affective disorders, neuromodulation, and brain-computer interface translation. He pioneered EEG-based functional brain network research in China and led the development of both the E-CDS and an integrated high-density EEG-TMS platform for precision neuromodulation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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