NYCU Inst. BioInfo & SysBio 生物資訊及系統生物研究所    NYCU College of Biological Science and Technology 生物科技學院    中文 English 

實驗室簡介

Lab Introduction

 神經工程實驗室研究核心著重於「腦機介面」的發展與應用,研究範圍從前端真實環境神經造影基礎研究至後端人機互動及臨床醫學應用科學,跨生物醫學與電機資訊等領域結合計算型智慧技術,研發創新科技於開創生醫資訊科技 (Bio-IT) 之應用,與加州大學聖地牙哥分校、雪梨科技大學及美國陸軍實驗室密切國際合作。

Neural Engineering Lab (NEL) is now focusing on the development and application of brain-computer interface (BCI), encompassing research of real world neuroimaging and various advanced applications including in clinical settings. Integrating research groups from biomedical sciences, electrical engineering and computer sciences, NEL is developing an inovative Bio-IT for biomedical applications. NEL is closely cooperating with University of California San Diego (UCSD), University of Technology Sydney (UTS), and United States Army Research Laboratory (U.S. ARL).




研究方向

Research Topics

系統開發

System Development

»腦波乾式電極與無線腦機界面系統研究

»Dry Electrodes for Wireless EEG System

本團隊與國家中山科學研究院合作研發了數位戰士智慧頭盔,雙層電路板和新穎海綿電極,並開發以穩態視覺誘發電位(SSVEP) 偵測使用者持續性專注力,分類效能達90%以上,另成功整合SSVEP及運動想像(Motor Imagery, MI)開發單一通道之混合式腦機介面系統,並與雪梨科技大學合作首創多模組模糊積分之機器學習於腦機介面系統架構,藉由各腦區認知功能的融合以調合不同腦區頻譜特徵,能有效提升系統準確度。並發表在2019 IEEE CIM(IF: 6.611)期刊上。

Our team developed a digital warrior smart helmet in cooperation with the NCSIST. The smart helmet consists of novel electrodes and an internal two-layer signal processing circuit enabling it to acquire EEG signals and perform brain-computer interface (BCI) applications. We developed a steady-state visual evoked potential (SSVEP) brain computer interface to detect the sustained attention of users, achieving a classification performance of more than 90%. Additionally, we successfully integrated SSVEP and Motor Imagery (MI) techniques to develop a single-channel, hybrid brain-computer interface system and incorporated multi-module fuzzy integral machine learning into the brain-computer interface system architecture, which improves the BCI system accuracy by integrating of activities of different brain regions and spectral features. This study was published in the IEEE CIM journal (IF: 6.611).


數位戰士智慧頭盔 (與國家中山科學研究院合作)

(L.-W. Ko et al., Sensors 2019.)


Smart Helmet for Digital Warrior (cooperation with NCSIST)

(L.-W. Ko et al., Sensors 2019.)

♦吸水性新式腦波感測海綿電極

♦整合新式電極與無線腦波量測裝置

♦舒適方便使用且依然具有高準確度效能

♦New moisturized sponge EEG sensors

♦Integration of developed sensors with wireless EEG recording system

♦Comfortable, convenient and high precision


複合式適合性腦機界面

(L.-W. Ko et al., IEEE TNSRE .2019)


Integrated Adaptive BCI

(L.-W. Ko et al., IEEE TNSRE .2019)

♦通過雙通道EEG共振頻率模式增強混合BCI性能

♦Simultaneous dual mental tasks to enhance BCI performance


多模態模糊理論增進腦機介面系統效能 (與雪梨科技大學合作)

(L.-W. Ko et al., IEEE CIM 2019. IF: 6.611 )


Multimodal Fuzzy Fusion to Enhance BCI Performance (cooperation with UTS)

(L.-W. Ko et al., IEEE CIM 2019. IF: 6.611 )

♦多模態模糊融合腦機介面架構,使用網路公開之BCI資料或是自行錄製之BCI資料,準確率皆高於傳統BCI系統

♦可結合低通道腦機介面應用於日常及臨床醫療

♦Better Performance comparing to conventional BCI systems

♦Potential daily and clinical application.

神經造影

Neuroimaging

»真實環境下人類神經造影研究

»Steady-State Visually Evoked Potential (SSVEP) BCI

本團隊為了瞭解真實環境下的抑制網絡而設計了具有威脅性的戰爭場景。並進行EEG和fMRI訊號同步量測實驗,在時域與空域了解人類抑制行為時大腦區域活化現象。除此之外,突破以往侷限於實驗室所進行之研究,在真實教室環境下探討學生於課堂上專注力之大腦認知狀態(與美國陸軍實驗室合作), 作為未來真實環境下腦機介面系統的腦波擷取特徵,對應用研究之腦機介面系統研發具相當助益,對探索人類認知功能之「持續性專注力」腦功能研究具突破性。

Our team designed a simulation of a realistic battlefield scenario to understand the brain’s suppression network in realistic environments. In cooperation with US ARL and UCSD, we investigated human cognition in realistic environments, expanding beyond the well-controlled environments historically preferred for cognitive research, to prepare for the future of BCI applications in the noisy and complicated real world. Using simultaneous EEG and fMRI, we investigated brain inhibitory activity in both temporal and spatial domains, as part of developing BCI applicable to real world scenarios and a breakthrough in the study of sustained attention in human cognition.


探討模擬戰爭場景下行為抑制網絡 (與美國陸軍實驗室合作)

(L.-W. Ko et al., Front. Hum. Neurosci. 2016,2018//R.K. Chikara& L.-W. Ko. Brain sciences 2019//R.K. Chikara& L.-W. Ko. Sensors .2019)


Inhibitory brain network in simulated battlefield scenario (cooperation with U.S. ARL)

(L.-W. Ko et al., Front. Hum. Neurosci. 2016,2018//R.K. Chikara& L.-W. Ko. Brain sciences 2019//R.K. Chikara& L.-W. Ko. Sensors .2019)

♦設計具有威脅性的場景-戰爭場景

♦進行EEG和fMRI訊號同步量測

♦從時域和空域了解人類抑制行為時大腦區域活化現象

♦Simulating a realistic battlefield scenario

♦Simultaneous measuring of EEG and fMRI

♦Investigating brain inhibitory activity in temporal and spatial domain.


探討真實教室環境下上課專注力之大腦狀態 (與美國陸軍實驗室 及 加州大學聖地牙哥分校合作)

(L.-W. Ko et al., Front. Hum. Neurosci. 2017. Citation: 31//Komarov, O et al. IEEE TNSRE .2019)


Sustained attention in real classroom settings: An EEG Study (cooperation with U.S. ARL and UCSD)

(L.-W. Ko et al., Front. Hum. Neurosci. 2017. Citation: 31//Komarov, O et al. IEEE TNSRE .2019)

♦真實教室環境下探討學生於課堂上專注力之大腦認知狀態

♦作為未來真實環境下腦機介面系統的腦波擷取特徵

♦Investigated students' attention in real classroom setting and corresponding EEG activity

♦Potential daily and clinical application.

人機互動

Brain-computer-interface

»穩態視覺誘發電位之腦機介面
(L.-W. Ko et al., IEEE TNSRE 2016. Cover Letter)

»Steady-State Visually Evoked Potential (SSVEP) BCI

隨著科技發展,近年來腦機介面技術日益精進,腦波控制開始受大家注目。本系統以穩態視覺誘發電位 技術開發,畫面中可同時發出多種不同頻率的閃爍,使用者可擇其一注視,此時人腦枕葉區會釋放相同 頻率信號。透過腦波信號成分關聯性分析,可快速回推使用者注視的項目為何者,進而達到腦波控制玩 遊戲的效果。

With the ever-changing nature of science, and the improvement of BCI technology, brain wave control has started to become famous. Based on SSVEP technology, this system evokes different frequency's signal from our Occipital Lobe with icons on the screen flickering in different frequency. By analyzing the signals, we can play the game with our brain waves.

♦專注力分類校能達90%以上,獲IEEE TNSRE期刊2016年第3期封面(cover letter)

♦結合VR與SSVEP-腦機介面,獲2017年光寶創新獎技術組銅賞

♦未來可廣泛應用於遊戲控制、智慧醫療輔具開發與智能家庭應用

♦Attention level classification accuracy up to 90%, published as cover letter in IEEE TNSRE 3rd issue on year 2016

♦VR and SSVEP integration system received bronze price in Lite-On Award 2017.

♦Potential future application in BCI-based gaming, smart medical assistive device and smart home

»智慧機器人圍棋共學系統(與台南大學合作)
(Chang-Shing Lee et al., IEEE SMC 2018.)

»Human and Smart Machine Co-Learning in Go Game
(Chang-Shing Lee et al., IEEE SMC 2018.

(cooperation with National University of Tainan)

♦結合腦波生理狀態即時量測與Alpha Go

♦即時監測生理狀態以調整訓練難度

♦獲得科技部未來科技突破獎

♦Attention level classification accuracy up to 90%, published as cover letter in IEEE TNSRE 3rd issue on year 2016

♦VR and SSVEP integration system received bronze price in Lite-On Award 2017.

♦Potential future application in BCI-based gaming, smart medical assistive device and smart home

臨床實驗

Brain-computer-interface

本團隊在臨床神經性疾病方面有不同的研究應用,如與馬偕紀念醫院合作,分析患者之腦波特徵,結合機器學習等技術,開發ADHD輔助診斷技術;與臺北榮總醫院合作,利用偏頭痛不同時期的腦波特徵,經過機器學習訓練開發輔助預測偏頭痛發作時期;與高雄醫學大學合作,分析輪班工作人員的睡眠腦波特徵,藉此找出容易因輪班作息影響認知能力與不容易被影響著之差異;與高雄醫學大學復健科合作,透過腦波檢測技術,運用Notch姿態感測器與混合實境(Mixed Reality)復健訓練遊戲進行虛實互動,整合擴增實境與功能性電刺激之神經復健腦機界面,輔助中風病患進行訓練。且下肢復健相關研究已獲得中華民國第I661802號專利,另本團隊研發新世代下肢復建系統獲得第十六屆國家新創獎。

Our team has multiple research applications and collaborations on clinical neurological diseases. In cooperation with Mackay Memorial Hospital, we analyzed patients' brainwave features, which were combined with machine learning techniques to develop technology for assisting in ADHD diagnosis; in cooperation with Taipei Veterans General Hospital, we identified neurological biomarkers during different periods of migraine and trained machine learning alrogirthms to predict migraine attacks; in cooperation with Kaohsiung Medical University, we analyzed the sleep brainwaves of shift workers, to investigate the differences between cognitive abilities that are most easily impacted by shift work. Also, in cooperation with Kaohsiung Medical University, we utilized simlultaneious brainwave detection technology, body motion capture, functional electrical stimulus, and mixed reality rehabilitation training games to assist stroke patients in rehabilitation training via virtual interactions. The related research on lower limb rehabilitation has obtained the patent No. I661802 of the Republic of China, and the integration of augmented reality and functional electrical stimulation in the neural rehabilitation brain-computer interface has been awarded the 16th National Innovation Award.


»機械手臂輔助進食與多媒體系統(與雪梨科技大學合作)

(C. T. Lin & L.-W. Ko et al. IEEE TCDS 2019.)

»Assistive Robotic Arm for Feeding and Multimedia System(cooperation with UTS)
(C. T. Lin & L.-W. Ko et al. IEEE TCDS 2019.

♦無線腦波量測系統擷取腦波訊號

♦以Unity遊戲引擎開發多層軟體選單

♦藉由醫用機械手臂協助使用者飲食、網路視訊、觀看影片、收看網路新聞與主動聲音訊息功能

♦Brain signals were measured from wireless EEG system

♦Multilayer software menu was developed in Unity

♦Use of medical robotic arm to assist in feeding, operating video cam and phone, watching video and news


»新世代下肢復建系統(與高雄醫學大學復健科合作)

(Wei-Chiao Chang et al., JSID 2019. Distinguished paper award)

»Next-Generation Lower Limb Rehabilitation System(cooperation with Kaohsiung Medical University)
(Wei-Chiao Chang et al., JSID 2019. Distinguished paper award)

♦整合擴增實境與功能性電刺激之神經復健腦機界面

♦結合步態量測與音樂擴增實境

♦同時監測病患大腦神經復原情形

♦獲最佳論文獎於2019 Display Week

♦獲第十六届國家新創獎

♦Integration of gait monitoring system and MR music game

♦Raising motivation of stroke patients doing lower limb rehabilitation

♦EEG monitoring system for post-stroke brain recovering progress

♦Distinguished paper award of 2019 Display Week

♦National Innovative Award 2019


肌電訊號手勢辨識與骨架追蹤之新型人機介面

Man-Machine Interface: EMG based Gesture Recognition Control and Skeleton Tracking

一直以來類似Kinect等體感裝置受制於深度影像限制,無法實現同時使用手勢辨識與骨架追蹤之人機介面,以致操作體驗上不直 覺不靈活。本新型人機介面利用肌電訊號分析技術製作手勢辨識裝置,結合深度影像裝置Kinect,實現同時使用手勢辨識和骨架 追蹤之效能,以遊戲操作為體驗,讓使用者體驗更直覺更靈活之嶄新操作介面,感受更高的高自由度、直覺性及沉浸感的虛擬實 境遊戲。

Motion sensing controller has been limited by Depth Image, which makes it impossible to achieve gesture recognition control and skeleton tracking at the same time, and cause inconvenient user experiences. With our new man-machine interface, combining EMG based gesture recognition control and Kinect, a Depth Image device, we successfully achieve gesture recognition control and skeleton tracking at the same time and let our users experienced a more convenient and high DOF virtual reality game.

疲勞監測系統

Fatigue Monitoring System

交通事故的發生率又以疲勞駕駛占了極大比例,若能藉由生理醫學電訊號得知駕駛人目前的生理狀態,評估駕駛行為下之即時反 應時間,提醒駕駛人以提高警覺性,可達到有效降低疲勞駕駛所導致的事故率。本系統以無線乾式電極腦波帽搭配即時線性運算, 同步分析腦電及眼電訊號特徵,開發出一套可靠且精準的駕駛疲勞評估演算法。可即時分析駕駛者的即時反應時間,再推算疲勞 狀態的變化,進而對駕駛者發出警報,此即時監測神經反饋系統,使人類的安全及生活更加多一分保障。

Among all the traffic accidents, the rate of fatigue driving is the highest. If we can evaluate our physiological state by physiological signals in time, we can warn the drivers and lower the rate of traffic accidents. Combining wireless EEG cab, eye tracking device, and our algorithm, we have a precise neural feedback system which can warn the drivers immediately and makes our life safer.

探討模擬戰爭場景下行為抑制網絡

To Study our Behavioral Inhibition System in Battlefield Simulator

先前研究多使簡易符號設計實驗,並已對抑制網絡系統有了一定的了解,本研究為了可瞭解真實環境下的抑制網絡設計了具有威脅 性的場景-戰爭場景。本研究發現前運動輔助區(preSMA) 、右側額葉中迴(rMFG)及右側額葉下迴(rIFG),皆在傳統符號場景下 與戰場模擬場景下成功抑制時有正向活化。fMRI結果也指出當人類在威脅情況下抑制行為時, 右側顳頂交界腦區(right temporoparietal junction, TPJ)將涉及抑制控制的調解。

Previous research studied behavioral inhibition system (BIS) with a very simple task. In our research, we simulated a very real and threatening circumstances to study BIS. The research indicates that preSMA, rMFG, rIFG are activated when inhibiting. Also, by fMRI, we found that the activation of right temporoparietal junction is related to inhibition control under threatenings.

生命徵象(心跳、體溫、呼吸)監測系統

Vital Signs Monitoring System

現今每位護士照顧人數高達13位病患,醫院固定每4小時循房一次,並量測病患之心跳、呼吸及體溫,相當耗時卻不能讓病患得到 更好的醫療照護。我們開發出一套系統,只需得到心電圖及「體表」體溫,即可推算出心跳數、呼吸數及「核心」體溫,並即時將 數據以無線傳輸傳送至護理端,藉此不僅能減少護理人員的工作量,每位病患也能得到更好的醫療品質。

Every nurse has to take care of 13 patient on average, and makes their rounds every 4 hours. Heart rate, respiration and body temperature are measured, which takes a lot of time. We've developed a system with only ECG signals and shell temperature required, we can calculate the patients' heart rate, respiration and core temperature. Combining the algorithm with wireless system, we can reduce the nurse's workload and enhance the healthcare quality.

智慧芳香舒眠系統

Smart Sweet-Scented Sleep System

睡眠在身心各方面有著關鍵的影響,舉凡情緒、壓力、記憶及健康,都和睡眠息息相關。然而,睡眠品質不佳是現代人的通病, 據台灣睡眠醫學會調查,國人睡眠滿意度從2000年的89%下降至2009年的63%,並統計出現今有高達六成的國人擁有失眠現象。 為解決大眾的睡眠問題,以入睡過程、睡眠深度、最佳起床時機等角度切入,開發並設計出此套居家型的「智慧芳香舒眠系統」。 並發展一套睡眠專家系統結合無線腦波帽系統(Mindo-4s)與睡眠軟體(iSleep),即時評估睡眠周期,讓使用者在結束量測時可 立即得到整晚的睡眠品質評估報告。

Sleeping affects our emotion, stress, memory, which plays an important role in our life. According to the Taiwan Society of Sleep Medicine, 60percent of the Taiwanese have Insomnia. In order to solve the sleeping problems, we've developed a Smart Sweet-Scented Sleep System. Combining wireless EEG cab and the iSleep software, it can evaluate our sleep quality from many different aspects, and it will be easier for users to find out where the problem is.