博碩士論文 109522032 詳細資訊




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姓名 王俊隆(Jun-Long Wang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 評估注意力偵測之穿戴式腦電電極放置有效性
(Evaluating the effectiveness of wearable EEG electrode placement for attention detection)
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摘要(中) 多動症的問題在世界越來越嚴重。注意力檢測問題與對多動症患者的評
估密切相關,它決定了受試者的集中程度。人腦的額葉和前額葉決定受試者
的注意力。其中,反應抑制是注意力的關鍵。它可以控制衝動的反應。在本
實驗中,我們進行了與反應抑制有關的任務,並收集了正常成年人的數據,
使用可穿戴式腦電設備OpenBCI Ganglion,集成並結合VR 一體機進行實
驗,並結合之前研究的腦電(Looxid)電極。根據國際10-20 系統的腦電定
位電極,對額頭的AF4、FP7、FP8、AF7、AF8 進行了比較,通過機器學習
驗證了目前應用於特定三點的可穿戴腦電設備FCz、FC3、Pz 在注意力檢測
方面的有效性。此外,我們還通過特徵探索了EEG 和眼球追踪之間的相關
性。最後,我們成功地確定了我們的EEG 電極位置的更好的有效性。
摘要(英) The problem of ADHD is getting more and more serious in the world. The
attention detection problem is closely related to the assessment of ADHD patients
that determines how concentrated the subject is. The frontal and prefrontal of the
human brain decide the subjects’ attention. Among them, response inhibition is
the key to attention. It can control impulsive responses. In this experiment, we
conduct a task related to response inhibition and we collected the data from
normal adults, and use the wearable EEG device OpenBCI Ganglion, integrated
and combined with the VR all-in-one machine to conduct the experiment, and
combined with the EEG (Looxid) electrode of the previous study. According to
the international 10-20 system for EEG location electrodes, AF4, FP7, FP8, AF7,
and AF8 in the forehead are compared, and we validate the effectiveness of the
current wearable EEG devices that applied to specific three points are FCz, FC3,
and Pz in attention detection through Machine Learning. Moreover, the
correlation between EEG and eye-tracking is explored through features. Finally,
we successfully determine the better effectiveness of our EEG electrode
placement.
關鍵字(中) ★ 腦電圖
★ 數據融合
★ 多模態數據
★ 虛擬現實
★ 機器學習
★ 眼睛追蹤
關鍵字(英) ★ Electroencephalography
★ Data Fusion
★ Multimodal Data
★ Virtual reality
★ Machine Learning
★ Eye-tracking
論文目次 Table of Contents
摘要
I
Table of Contents IV
List of Figures V
List of Tables VII
Introduction 1
Related Works 8
Go/NoGO task 8
EEG/Eye correlation 8
Attention Detection 9
Methodologies 11
A. Experiment 11
B. Data Preprocess and Features Extraction 15
C. Correlation Coefficient 18
D. Machine Learning Method 18
Results 22
Conclusion and Discussions 27
Reference 28
參考文獻 [1] Biederman, J. (2005). Attention-deficit/hyperactivity disorder: A selective overview. Biological Psychiatry, 57(11), 1215–1220.
https://doi.org/10.1016/j.biopsych.2004.10.020
[2] Siamaknejad, H., Liew, W.S. & Loo, C.K. Fractal dimension methods to determine optimum EEG electrode placement for concentration
estimation. Neural Comput & Applic 31, 945–953 (2019). https://doi.org/10.1007/s00521-017-3126-1
[3] Lutzenberger W, Elbert T, Birbaumer N, Ray WJ, Schupp H (1992) The scalp distribution of the fractal dimension of the EEG and its
variation with mental tasks. Brain Topogr 5(1):27–34
[4] Duncan, C. C., Barry, R. J., Connolly, J. F., Fischer, C., Michie, P. T., Näätänen, R., Polich, J., Reinvang, I., & Van Petten, C. (2009). Eventrelated
potentials in clinical research: Guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clinical
Neurophysiology, 120(11), 1883–1908. https://doi.org/10.1016/j.clinph.2009.07.045
[5] Edwards, M. C., Gardner, E. S., Chelonis, J. J., Schulz, E. G., Flake, R. A., & Diaz, P. F. (2007). Estimates of the Validity and Utility of the
Conners’ Continuous Performance Test in the Assessment of Inattentive and/or Hyperactive-Impulsive Behaviors in Children. Journal of
Abnormal Child Psychology, 35(3), 393–404. doi:10.1007/s10802-007-9098-3
[6] Lundervold, A. J., Stickert, M., Hysing, M., Sørensen, L., Gillberg, C., & Posserud, M.-B. (2016). Attention deficits in children with
combined autism and adhd: A cpt study. Journal of Attention Disorders, 20(7), 599–609. https://doi.org/10.1177/1087054712453168
[7] Khoshnoud, S., Nazari, M. A., & Shamsi, M. (2018). Functional brain dynamic analysis of ADHD and control children using nonlinear
dynamical features of EEG signals. Journal of Integrative Neuroscience, 17(1), 17–30. doi:10.3233/jin-1700331
[8] Rezaeezadeh, M., Shamekhi, S., & Shamsi, M. (2020). Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and
multivariate EEG measurements: a preliminary study. Physical and Engineering Sciences in Medicine, 43(2), 577–592. doi:10.1007/s13246-
020-00858-31
[9] Johnstone, S. J., Pleffer, C. B., Barry, R. J., Clarke, A. R., & Smith, J. L. (2005). Development of Inhibitory Processing During the Go/NoGo
Task: A Behavioral and Event-Related Potential Study of Children and Adults. Journal of Psychophysiology, 19(1), 11–23.
https://doi.org/10.1027/0269-8803.19.1.111
[10] P. Putra, K. Shima and K. Shimatani, "Catchicken: A Serious Game Based on the Go/NoGo Task to Estimate Inattentiveness and Impulsivity
Symptoms," 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, pp. 152-157, doi:
10.1109/CBMS49503.2020.000361
[11] Hermens DF, Soei EX, Clarke SD, Kohn MR, Gordon E, Williams LM. Resting EEG theta activity predicts cognitive performance in
attention-deficit hyperactivity disorder. Pediatr Neurol. 2005 Apr;32(4):248-56. doi: 10.1016/j.pediatrneurol.2004.11.009. PMID:
15797181.1
[12] Chen, Y., Huang, X., Luo, Y., Peng, C., & Liu, C. (2010). Differences in the neural basis of automatic auditory and visual time perception:
ERP evidence from an across-modal delayed response oddball task. Brain Research, 1325, 100–111.
https://doi.org/10.1016/j.brainres.2010.02.0401
[13] D. Szajerman and P. Napieralski, "Joint analysis of simultaneous EEG and eye tracking data for video picture," 2017 18th Inte rnational
Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering (ISEF) Book of Abstracts, 2017, pp. 1-2, doi:
10.1109/ISEF.2017.8090693.1
[14] G. Tan, K. Xu, J. Liu and H. Liu, "A Trend on Autism Spectrum Disorder Research: Eye Tracking-EEG Correlative Analytics," in IEEE
Transactions on Cognitive and Developmental Systems, doi: 10.1109/TCDS.2021.3102646.1
[15] S. Kaheh, M. Ramirez, J. Wong and K. George, "Neuromarketing using EEG Signals and Eye-tracking," 2021 IEEE International Conference
on Electronics, Computing and Communication Technologies (CONECCT), 2021, pp. 1-4, doi: 10.1109/CONECCT52877.2021.9622539.
[16] Schneiders, E., Kristensen, M. B., Svangren, M. K., & Skov, M. B. (2020). Temporal impact on cognitive distraction detection for car drivers
using eeg. 32nd Australian Conference on Human-Computer Interaction, 594–601. https://doi.org/10.1145/3441000.3441013
[17] E. Dolezalek, M. Farnan and C. -H. Min, "Physiological Signal Monitoring System to Analyze Driver Attentiveness," 2021 IEEE
International Midwest Symposium on Circuits and Systems (MWSCAS), 2021, pp. 635-638, doi: 10.1109/MWSCAS47672.2021.9531871.
[18] N. Sokhn, A. Wuilleret and R. Caldara, "Go/No-Go Saccadic Reaction Times Towards Visual Field Targets Differ Between Athletes and
Nonathletes," 2019 11th International Conference on Knowledge and Smart Technology (KST), 2019, pp. 222-225, doi:
10.1109/KST.2019.8687809.
[19] 陳李治(2009)。同儕教導射擊訓練課程對ADHD學生學習行為與社會行為之研究。國立臺灣師範大學體育學系在職進修碩士
班碩士論文,台北市。 取自https://hdl.handle.net/11296/ad325t
[20] Y. Tan et al., "Virtual Classroom: An ADHD Assessment and Diagnosis System Based on Virtual Reality," 2019 IEEE International
Conference on Industrial Cyber Physical Systems (ICPS), 2019, pp. 203-208, doi: 10.1109/ICPHYS.2019.8780300.
[21] Gonthier, C., Macnamara, B. N., Chow, M., Conway, A. R. A., & Braver, T. S. (2016). Inducing proactive control shifts in the ax-cpt.
Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01822
[22] R. Chai, M. R. Smith, T. N. Nguyen, S. H. Ling, A. J. Coutts and H. T. Nguyen, "Comparing features extractors in EEG-based cognitive
fatigue detection of demanding computer tasks," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and
Biology Society (EMBC), 2015, pp. 7594-7597, doi: 10.1109/EMBC.2015.7320150.
[23] A. Taymourtash and F. Ghassemi, "Neurophysiological correlates of inhibitory control in adults with ADHD revealed by iterative
independent component analysis," 2014 21th Iranian Conference on Biomedical Engineering (ICBME), 2014, pp. 58-63, doi:
10.1109/ICBME.2014.7043894.
[24] F. Ghassemi, M. H. Moradi, M. Tehrani-Doost and V. Abootalebi, "Classification of ADHD/normal participants using frequency features
of ERP′s Independent Components," 2010 17th Iranian Conference of Biomedical Engineering (ICBME), 2010, pp. 1-4, doi:
36
10.1109/ICBME.2010.5704916.
[25] A. D. -A. Abdullah and A. Q. Al-Neami, "Performance Evaluation of a New Dry-Contact Electrode for EEG Measurement," 2021 IEEE
International Biomedical Instrumentation and Technology Conference (IBITeC), 2021, pp. 119-123, doi:
10.1109/IBITeC53045.2021.9649333.
[26] Lopez-Gordo, M. A., D. Sanchez-Morillo, and F. Pelayo Valle. 2014. "Dry EEG Electrodes" Sensors 14, no. 7: 12847-12870.
https://doi.org/10.3390/s140712847
[27] R. Chai, M. R. Smith, T. N. Nguyen, S. H. Ling, A. J. Coutts and H. T. Nguyen, "Comparing features extractors in EEG-based cognitive
fatigue detection of demanding computer tasks," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and
Biology Society (EMBC), 2015, pp. 7594-7597, doi: 10.1109/EMBC.2015.7320150.
[28] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018). Eegnet: A compact convolutional
network for eeg-based brain-computer interfaces. Journal of Neural Engineering, 15(5), 056013. https://doi.org/10.1088/1741-2552/aace8c
[29] F. Klotzsche, A. Mariola, S. Hofmann, V. V. Nikulin, A. Villringer and M. Gaebler, "Using EEG to Decode Subjective Levels of Emotional
Arousal During an Immersive VR Roller Coaster Ride," 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2018, pp.
605-606, doi: 10.1109/VR.2018.8446275.
[30] F. Klotzsche, A. Mariola, S. Hofmann, V. V. Nikulin, A. Villringer and M. Gaebler, "Using EEG to Decode Subjective Levels of Emotional
Arousal During an Immersive VR Roller Coaster Ride," 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2018, pp.
605-606, doi: 10.1109/VR.2018.8446275.
[31] . Xu, R. Yin, L. Shu and X. Xu, "Emotion Recognition Using Frontal EEG in VR Affective Scenes," 2019 IEEE MTT-S International
Microwave Biomedical Conference (IMBioC), 2019, pp. 1-4, doi: 10.1109/IMBIOC.2019.8777843.
[32] F. Tian and W. Zhang, "Difference of Emotional Arousal Between Real Shooting and Cartoon Pictures in VR Environment -A
Comparative Study Based on EEG Signals," 2021 IEEE 3rd International Conference on Frontiers Technology of Information and
Computer (ICFTIC), 2021, pp. 547-550, doi: 10.1109/ICFTIC54370.2021.9647399.
[33] B. Ivanovski and G. S. Malhi, “The psychological and neurophysiological concomitants of mindfulness forms of meditation,” Acta
Neuropsychiatrica, vol. 19, pp. 76-91, 2007.
[34] N.-H. Liu, C.-Y. Chiang, and H.-C. Chu (2013), “Recognizing the degree of human attention using eeg signals from
mobile sensors,” Sensors (Basel, Switzerland), vol. 13, no. 8, pp. 10 273–10 286
[35] Bear, M. F., Connors, B. W., & Paradiso, M. A. (2016). Neuroscience: Exploring the brain (Fourth edition). Wolters Kluwer.
[36] Jasper H (1958) Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr Clin
Neurophysiol 10:370–375
指導教授 葉士青 吳曉光(Shih-Ching Yeh Hsiao-Kuang Wu) 審核日期 2022-8-22
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