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研究生: 呂偉誠
Lu, Wei-Cheng
論文名稱: 穿戴式神經回饋訓練系統開發與驗證
Development and Verification of Wearable Neurofeedback Training Systems
指導教授: 梁勝富
Liang, Sheng-Fu
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 47
中文關鍵詞: 神經回饋訓練腦電圖前額眨眼穿戴式裝置無線
外文關鍵詞: Neurofeedback training, EEG, Forehead, Eye blink, Wearable device, wireless
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  • 神經回饋訓練(Neurofeedback training, NFT)為利用儀器可觀察腦部的活動和量測腦電訊號,其主要的目的就是透過操作型制約(Operant condition)讓使用者可以察覺大腦特定的活性進而讓使用者能夠從中自我學習來控制自我的腦波。頭皮腦電圖(Electroencephalogram, EEG)的alpha節律落在8-12Hz的頻率範圍內,alpha節律的經典功能它被認為與皮質抑制有關,目前已經廣泛討論了alpha節律在不同腦區的功能作用,異常或不對稱的alpha腦活動被認為與精神障礙有關,例如焦慮或抑鬱、失眠患者。額頂區域的alpha活動與智力和記憶表現密切相關。其中由於失眠被認為是在一整天過程中處於過度覺醒(Hyperarousal)的異常狀態,可能是因交感神經過度興奮或伴隨副交感神經活性不夠興奮所引起,所以如果可以利用大腦電位活性來做為回饋訊號 並直接調整大腦的運作情況,似乎更能夠直接去探討在正常情況與疾病狀態下之腦部運轉功能,因此利用神經回饋治療的方式,直接調整中樞神經系統活性,應該更有效地改善中樞神經相關之異常行為。
    以往神經回饋訓練大多僅使用在實驗室或是臨床研究上,受試者需戴電極帽及打上導電凝膠,經電極線送到電腦,使用特定程式便可把訊號顯示或是回饋。在使用方面,受試者不能自行操作,必須要專業人士的協助,耗時較長。而且因為有線的關係,碰到電極線或是輕微的拉扯都會造成干擾,受試者在開始實驗後活動便會受到限制,不方便於訓練期間的休息。因此目前市面上也開始有相關穿戴式神經回饋或透過腦波治療睡眠問題的裝置,但目前都有些問題,例如訊號準確性、無法有效對有失眠的患者。
    因此本研究開發一套方便穿戴且訊號準確性經過驗證的系統,讓使用者能自主訓練,經過驗證結果發現,以前額腦電訊號NFT系統進行訓練,其相關指標也能使頭皮腦波有相同趨勢的效果,因此本系統能告知受試者是否成功發出指定的波長,並達到訓練的效果。

    Neurofeedback training (NFT) is an instrument that allows observation of brain activity and measurement of brain electrical signals. Its main purpose is to allow users to perceive brain-specific activity through operational conditions. In turn, the user can learn from himself to control the brainwave of the self. The alpha rhythm of the scalp electroencephalogram (EEG) falls within the frequency range of 8-12 Hz. The classical function of alpha rhythm is thought to be related to cortical inhibition. The functional effects of alpha rhythm in different brain regions have been widely discussed. Asymmetric alpha brain activity is thought to be associated with mental disorders such as anxiety or depression, and insomnia. The alpha activity in the frontal area is closely related to intelligence and memory performance. Insomnia is thought to be an abnormal state of hyperarousal throughout the day, possibly due to sympathetic over-excited or accompanied by insufficient excitatory parasympathetic activity, so if brain potential activity can be used as feedback Signals and directly adjust the operation of the brain, it seems that it is more able to directly explore the function of the brain in normal conditions and disease states. Therefore, using neurofeedback therapy to directly adjust the activity of the central nervous system should improve the central nervous system more effectively. Related anomalous behavior.
    In the past, most of the neural feedback training was only used in laboratory or clinical research. Subjects should wear electrode caps and conductive gels, and send them to the computer via electrode lines. The signals can be displayed or fed back using a specific program. In terms of use, the subject is not able to operate by self and must be assisted by a professional and takes a long time. And because of the wired relationship, encountering the electrode line or a slight pull will cause interference, and the subject will be limited after the start of the experiment, and it is inconvenient to rest between training sessions. Therefore, there are currently devices on the market that are related to wearable nerve feedback or through brain waves to treat sleep problems, but there are some problems, such as signal accuracy, and ineffective for patients with insomnia.
    Therefore, this study developed a system that is easy to wear and the signal accuracy is verified, allowing users to self-train. After verification, it was found that the forehead EEG NFT system was trained and its related indicators could also make the scalp EEG have the same trend, so the system can tell the subject whether the specified wavelength is successfully issued and achieve the training effect.

    摘要 I Abstract III 誌謝 V List of Figures IX List of Tables XI Chapter 1 Introduction 1 1.1 Background 1 1.2 Related Works 3 1.2.1 Wearable devices 3 1.2.2 Alpha NFT with scalp EEG system 3 1.2.3 Related devices 4 1.3 Motivation and Objective 6 1.4 Thesis Overview 6 Chapter 2 System Design and Implementation 7 2.1 System Architecture 7 2.2 Hardware Design and Implementation 8 2.2.1 Headband Design 9 2.2.2 Electrode design and Position setting 10 2.2.3 Specifications of Hardware Implementation 14 2.2.4 Analog Front End Circuit (AFE) 16 2.2.5 Microcontroller 18 2.2.6 AFE power supply circuit 20 2.2.7 Microcontroller power supply circuit 20 2.2.8 Peripheral circuit 21 2.3 Firmware Design and Implementation 22 2.3.1 Data acquisition 24 2.3.2 Data storage 24 2.3.3 Wireless data transmission 24 2.3.4 Wireless data format 25 2.4 Software Design and Implementation 26 2.4.1 Artifact detection 27 2.4.2 Eye blinks frequency 29 2.4.3 Score 30 Chapter 3 Experiments and Results 32 3.1 Experiment Materials and Methods 32 3.1.1 Participants 32 3.1.2 Electrodes placement 32 3.1.3 Experiment flow 34 3.1.4 Analysis method 36 3.2 Results 38 3.2.1 EEG analysis 38 3.2.2 Memory performance 42 Chapter 4 Discussion 43 Chapter 5 Conclusions 45 Reference 46

    [1] W. Klimesch, P. Sauseng, and S. Hanslmayr, "EEG alpha oscillations: the inhibition–timing hypothesis," Brain research reviews, vol. 53, no. 1, pp. 63-88, 2007.
    [2] W. Heller, J. B. Nitschke, M. A. Etienne, and G. A. Miller, "Patterns of regional brain activity differentiate types of anxiety," Journal of abnormal psychology, vol. 106, no. 3, p. 376, 1997.
    [3] N. Jaimchariyatam, C. L. Rodriguez, and K. Budur, "Prevalence and correlates of alpha-delta sleep in major depressive disorders," Innovations in clinical neuroscience, vol. 8, no. 7, p. 35, 2011.
    [4] K. Cervena, F. Espa, L. Perogamvros, S. Perrig, H. Merica, and V. Ibanez, "Spectral analysis of the sleep onset period in primary insomnia," Clinical Neurophysiology, vol. 125, no. 5, pp. 979-987, 2014.
    [5] A. Anokhin and F. Vogel, "EEG alpha rhythm frequency and intelligence in normal adults," Intelligence, vol. 23, no. 1, pp. 1-14, 1996.
    [6] S. Hanslmayr, P. Sauseng, M. Doppelmayr, M. Schabus, and W. Klimesch, "Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects," Applied psychophysiology and biofeedback, vol. 30, no. 1, pp. 1-10, 2005.
    [7] O. Jensen and C. D. Tesche, "Frontal theta activity in humans increases with memory load in a working memory task," European journal of Neuroscience, vol. 15, no. 8, pp. 1395-1399, 2002.
    [8] W. Klimesch, "EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis," Brain research reviews, vol. 29, no. 2-3, pp. 169-195, 1999.
    [9] P. Sauseng, W. Klimesch, M. Doppelmayr, T. Pecherstorfer, R. Freunberger, and S. Hanslmayr, "EEG alpha synchronization and functional coupling during top‐down processing in a working memory task," Human brain mapping, vol. 26, no. 2, pp. 148-155, 2005.
    [10] J. H. Gruzelier, T. Thompson, E. Redding, R. Brandt, and T. Steffert, "Application of alpha/theta neurofeedback and heart rate variability training to young contemporary dancers: State anxiety and creativity," International Journal of Psychophysiology, vol. 93, no. 1, pp. 105-111, 2014.
    [11] T. Roth, "Insomnia: definition, prevalence, etiology, and consequences," Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine, vol. 3, no. 5 Suppl, p. S7, 2007.
    [12] S. J. Leigh, R. J. Bradley, C. P. Purssell, D. R. Billson, and D. A. Hutchins, "A simple, low-cost conductive composite material for 3D printing of electronic sensors," PloS one, vol. 7, no. 11, p. e49365, 2012.
    [13] "ADS1299-x Low-Noise, 4-, 6-, 8-Channel, 24-Bit, Analog-to-Digital Converter for EEG and Biopotential Measurements," 2017.
    [14] L. Toresano, S. K. Wijaya, Prawito, A. Sudarmaji, and C. Badri, "Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS1299," in AIP Conference Proceedings, 2017, vol. 1862, no. 1: AIP Publishing, p. 030149.
    [15] E. Mastinu, M. Ortiz-Catalan, and B. Håkansson, "Analog front-ends comparison in the way of a portable, low-power and low-cost EMG controller based on pattern recognition," in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015: IEEE, pp. 2111-2114.
    [16] S. S. S. Teja, S. S. Embrandiri, and N. Chandrachoodan, "EOG based virtual keyboard," in 2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), 2015: IEEE, pp. 1-2.
    [17] "nRF52840 Objective Product Specification v0.5," 2016.
    [18] "FC-135 32.7680KA-AC3," 2015.
    [19] "TPS6040x Unregulated 60-mA Charge Pump Voltage Inverter," 2015.
    [20] "TPS723xx-Q1 200-mA Low-Noise, High-PSRR, Negative-Output, Low-Dropout Linear Regulators," 2017.
    [21] "AP2112 600mA CMOS LDO REGULATOR WITH ENABLE," 2017.
    [22] "ADXL362 Micropower, 3-Axis, ±2 g/±4 g/±8 g Digital Output MEMS Accelerometer," 2012.
    [23] M. A. Sovierzoski, F. I. Argoud, and F. M. de Azevedo, "Identifying eye blinks in EEG signal analysis," in 2008 International Conference on Information Technology and Applications in Biomedicine, 2008: IEEE, pp. 406-409.
    [24] J. J. Hsueh, T. S. Chen, J. J. Chen, and F. Z. Shaw, "Neurofeedback training of EEG alpha rhythm enhances episodic and working memory," Hum Brain Mapp, vol. 37, no. 7, pp. 2662-75, Jul 2016, doi: 10.1002/hbm.23201.
    [25] W. Plihal and J. Born, "Effects of early and late nocturnal sleep on declarative and procedural memory," Journal of cognitive neuroscience, vol. 9, no. 4, pp. 534-547, 1997.
    [26] Q.-S. Wang and J.-N. Zhou, "Retrieval and encoding of episodic memory in normal aging and patients with mild cognitive impairment," Brain Research, vol. 924, no. 1, pp. 113-115, 2002.

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