{"id":32,"date":"2026-02-21T18:29:23","date_gmt":"2026-02-21T10:29:23","guid":{"rendered":"https:\/\/sioklab.com\/?page_id=32"},"modified":"2026-04-16T20:59:43","modified_gmt":"2026-04-16T12:59:43","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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[04\/2026] NeuroVoice Showcased at PolyU Alumni Day!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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We are delighted to share that the SIOK LAB of the Department of Language Sciences and Technology at The Hong Kong Polytechnic University was invited to participate in the PolyU Alumni Day showcase. At the event, our laboratory presented NeuroVoice<\/strong>, a non-invasive intelligent speech brain-computer interface system developed entirely in-house with full proprietary intellectual property.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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NeuroVoice is designed to help restore natural communication for individuals with speech impairments. It integrates a non-invasive wearable device<\/strong> with an intelligent analysis platform<\/strong> to monitor language-related brain activity, interpret neural signals, and translate them into speech and text in real time through an AI-powered model.<\/p>

During the showcase, many volunteers enthusiastically experienced the NeuroVoice system, which received highly positive feedback from scholars, teachers, students, and alumni alike. We are sincerely grateful for the long-standing support from the University, the Faculty, and the Department, as well as for the tireless dedication of our team members, whose hard work made this presentation possible. We would also like to extend our special thanks to the Dean of the Faculty of Humanities, Dean Hu, for the continued attention and support given to our research.<\/p>

The showcase also brought us many valuable suggestions from users, all of which our team greatly appreciates. We will continue refining the system and strive to launch an upgraded version of NeuroVoice in the near future, with the goal of better serving individuals with speech impairments.<\/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] UBSN Research Seminar: Prof. Yang Yang to Give a Talk at PolyU (17 April 2026)!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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We are pleased to share that our lab will host Prof. Yang Yang (Associate Professor, Institute of Psychology, Chinese Academy of Sciences)<\/strong> for a UBSN Research Seminar under the UBSN Capacity Building Scheme: Inbound Scheme at The Hong Kong Polytechnic University on 17 April 2026 (Friday)<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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In this talk, \u201cHow the Brain Learns to Read and Write \u2013 And Why Some Struggle,\u201d Prof. Yang will present recent findings on the developmental and evolutionary mechanisms of Chinese handwriting and reading based on functional and structural MRI studies. He will discuss how handwriting development is accompanied by focal functional specialization, increasing functional lateralization, and dynamic reconfiguration of cognitive, sensorimotor, and visual networks. He will also introduce cross-species evidence from humans and macaques that highlights the anatomical similarity and functional evolution of Exner\u2019s area, a shared brain locus involved in both reading and writing.<\/p>

In the second part of the talk, Prof. Yang will discuss the neural basis of writing deficits and their relationship to reading impairments in developmental dyslexia. He will present findings showing that children with dyslexia exhibit abnormalities in both regional activation and functional connectivity during handwriting, and that reduced activation in the left supplementary motor area and the right precuneus is linked to impairments in both handwriting and reading. He will also introduce a digital handwriting-based training program that significantly improves writing and reading skills, with transfer effects on attention abilities.<\/p>

\u2022 Date: 17 April 2026 (Friday) <\/strong>
\u2022 Time: 11:00 am\u201312:00 noon <\/strong>
\u2022 Venue: Room PQ303, PolyU <\/strong>
\u2022 Registration: Please register via the QR code on the poster.<\/strong><\/p>

We warmly welcome students and colleagues to join us!<\/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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[03\/2026] Congratulations to Gong\u2019s paper is accepted by IEEE Sensors Journal!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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S Gong, Y Li, Z Kang, B Chai, W Zeng, H Yan, Z Zhang, WT Siok, N Wang. LEREL: Lipschitz Continuity-Constrained Emotion Recognition Ensemble Learning For Electroencephalography [J]. IEEE Sensors Journal, 2026<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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

Accurate and efficient recognition of emotional states is critical for human social functioning, and impairments in this ability are associated with significant psychosocial difficulties. While electroencephalography (EEG) offers a powerful tool for objective emotion detection, existing EEG-based Emotion Recognition (EER) methods suffer from three key limitations: (1) insufficient model stability, (2) limited accuracy in processing high-dimensional nonlinear EEG signals, and (3) poor robustness against intra-subject variability and signal noise. To address these challenges, we introduce Lipschitz continuity-constrained Ensemble Learning (LEL), a novel framework that enhances EEG-based emotion recognition by enforcing Lipschitz continuity constraints on Transformer-based attention mechanisms, spectral extraction, and normalization modules. This constraint ensures model stability, reduces sensitivity to signal variability and noise, and improves generalization capability. Additionally, LEL employs a learnable ensemble fusion strategy that optimally combines decisions from multiple heterogeneous classifiers to mitigate single-model bias and variance. Extensive experiments on three public benchmark datasets (EAV, FACED, and SEED) demonstrate superior performance, achieving average recognition accuracies of 74.25%, 81.19%, and 86.79%, respectively. The official implementation codes are available at\u00a0https:\/\/github.com\/NZWANG\/LEL<\/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\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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[03\/2026] LST Research Seminar: Dr. Yuanning Li to Give a Talk at PolyU (24 March 2026)!<\/div>\t\t\t\t<\/div>\n\t\t\t\t
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We are pleased to share that our lab will host\u00a0Dr. Yuanning Li<\/strong>\u00a0(Assistant Professor, School of Biomedical Engineering,\u00a0ShanghaiTech University<\/strong>) for an\u00a0LST Research Seminar<\/strong>\u00a0at The Hong Kong Polytechnic University on\u00a024 March 2026 (Tuesday)<\/strong>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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We are pleased to share that our lab will host\u00a0Dr. Yuanning Li<\/strong>\u00a0(Assistant Professor, School of Biomedical Engineering,\u00a0ShanghaiTech University<\/strong>) for an\u00a0LST Research Seminar<\/strong>\u00a0at The Hong Kong Polytechnic University on\u00a024 March 2026 (Tuesday)<\/strong>.<\/p>

In this talk,\u00a0\u201cNeural coding, computational models and brain-computer interfaces for human languages,\u201d<\/strong>\u00a0Dr. Li will introduce recent computational efforts to understand and reconstruct speech perception and production using\u00a0human intracranial electrophysiology recordings<\/strong>\u00a0and\u00a0AI models<\/strong>. He will also discuss converging representations between biological speech networks and deep neural network models, and how tailored deep learning models can enable\u00a0speech brain\u2013computer interfaces<\/strong>\u00a0that synthesize speech directly from intracranial signals.<\/p>