BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Taipei BEGIN:STANDARD DTSTART:19790930T230000 TZOFFSETFROM:+0900 TZOFFSETTO:+0800 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260407T041056Z UID:D01DDC56-BA5F-4429-9DC6-88F714DE6ACA DTSTART;TZID=Asia/Taipei:20260326T100000 DTEND;TZID=Asia/Taipei:20260326T110000 DESCRIPTION:This topic explores how the integration of Artificial Intellige nce (AI) and Electroencephalography (EEG) is opening a new era in understa nding brain function. By applying advanced machine learning and deep learn ing techniques\, researchers can extract meaningful patterns from complex brain signals and analyze neural activity more accurately and efficiently. \n\nEEG is a non-invasive brain monitoring technique that records the elec trical activity generated by neurons in real time. It has been widely used in neuroscience research\, clinical diagnosis\, and brain–computer inte rface (BCI) applications. However\, EEG data are often high-dimensional\, noisy\, and complex\, making traditional analysis methods limited in their ability to fully interpret the underlying information.\n\nWith the integr ation of AI\, these challenges can be addressed more effectively. AI model s can automatically learn important features from EEG signals and apply th em to tasks such as neurological disease detection\, cognitive state analy sis\, mental health monitoring\, and neurofeedback-based therapies. This c ombination enables faster data processing\, improved diagnostic accuracy\, and the development of personalized medical solutions.\n\nOverall\, the c onvergence of AI and EEG is transforming brain research by enabling more p recise brain signal interpretation\, real-time monitoring\, and intelligen t healthcare applications\, paving the way for breakthroughs in neuroscien ce\, medicine\, and human–machine interaction.\n\nSpeaker(s): TP Jung\n\ nRoom: 108\, Bldg: ED\, No. 1001\, Daxue Rd. East Dist.\, Hsinchu\, T'ai-p ei\, Taiwan\, 30010 LOCATION:Room: 108\, Bldg: ED\, No. 1001\, Daxue Rd. East Dist.\, Hsinchu\, T'ai-pei\, Taiwan\, 30010 ORGANIZER:lichun@g2.nctu.edu.tw SEQUENCE:4 SUMMARY:Talk：Understanding the Brain: AI × EEG - A New Era URL;VALUE=URI:https://events.vtools.ieee.org/m/544996 X-ALT-DESC:Description: <br /><div class="flex flex-col text-sm pb-25">\n<a rticle class="text-token-text-primary w-full focus:outline-none [--shadow- height:45px] has-data-writing-block:pointer-events-none has-data-writing-b lock:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&a mp\;:has([data-writing-block])&gt\;*]:pointer-events-auto scroll-mt-[calc( var(--header-height)+min(200px\,max(70px\,20svh)))]" dir="auto" tabindex=" -1" data-turn-id="request-WEB:46dde0f8-f72f-4adf-95a7-a1c41ec0c4d9-1" data -testid="conversation-turn-4" data-scroll-anchor="true" data-turn="assista nt">\n<div class="text-base my-auto mx-auto pb-10 [--thread-content-margin :var(--thread-content-margin-xs\,calc(var(--spacing)*4))] @w-sm/main:[--th read-content-margin:var(--thread-content-margin-sm\,calc(var(--spacing)*6) )] @w-lg/main:[--thread-content-margin:var(--thread-content-margin-lg\,cal c(var(--spacing)*16))] px-(--thread-content-margin)">\n<div class="[--thre ad-content-max-width:40rem] @w-lg/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group/turn-messages focu s-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn" tabindex="-1">\n<div class="flex max-w-full flex-col gap-4 grow">\n<div c lass="min-h-8 text-message relative flex w-full flex-col items-end gap-2 t ext-start break-words whitespace-normal [.text-message+&amp\;]:mt-1" dir=" auto" data-message-author-role="assistant" data-message-id="277e3c59-a6f7- 4389-90e8-fdb17641b45d" data-message-model-slug="gpt-5-3">\n<div class="fl ex w-full flex-col gap-1 empty:hidden">\n<div class="markdown prose dark:p rose-invert w-full wrap-break-word light markdown-new-styling">\n<p data-s tart="64" data-end="430">This topic explores how the integration of Artifi cial Intelligence (AI) and Electroencephalography (EEG) is opening a new e ra in understanding brain function. By applying advanced machine learning and deep learning techniques\, researchers can extract meaningful patterns from complex brain signals and analyze neural activity more accurately an d efficiently.</p>\n<p data-start="432" data-end="853">EEG is a non-invasi ve brain monitoring technique that records the electrical activity generat ed by neurons in real time. It has been widely used in neuroscience resear ch\, clinical diagnosis\, and brain&ndash\;computer interface (BCI) applic ations. However\, EEG data are often high-dimensional\, noisy\, and comple x\, making traditional analysis methods limited in their ability to fully interpret the underlying information.</p>\n<p data-start="855" data-end="1 293">With the integration of AI\, these challenges can be addressed more e ffectively. AI models can automatically learn important features from EEG signals and apply them to tasks such as neurological disease detection\, c ognitive state analysis\, mental health monitoring\, and neurofeedback-bas ed therapies. This combination enables faster data processing\, improved d iagnostic accuracy\, and the development of personalized medical solutions .</p>\n<p data-start="1295" data-end="1576" data-is-last-node="" data-is-o nly-node="">Overall\, the convergence of AI and EEG is transforming brain research by enabling more precise brain signal interpretation\, real-time monitoring\, and intelligent healthcare applications\, paving the way for breakthroughs in neuroscience\, medicine\, and human&ndash\;machine intera ction.</p>\n</div>\n</div>\n</div>\n</div>\n<div class="z-0 flex min-h-[46 px] justify-start">&nbsp\;</div>\n<div class="mt-3 w-full empty:hidden">\n <div class="text-center">&nbsp\;</div>\n</div>\n</div>\n</div>\n</article> \n</div>\n<div class="pointer-events-none h-px w-px absolute bottom-0" ari a-hidden="true" data-edge="true">&nbsp\;</div> END:VEVENT END:VCALENDAR