博碩士論文 108825001 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:87 、訪客IP:3.144.123.7
姓名 陳彥勳(Yen-Hsun Chen)  查詢紙本館藏   畢業系所 認知與神經科學研究所
論文名稱
(Exploration of Alpha Entrainment Effect on Flanker Task Performance)
相關論文
★ 時間及空間對注意力暫失的影響 以及其可能的神經生理機制★ 注意力分配及眼球運動準備歷程對於眼動潛伏時間與眼動軌跡的影響
★ 注意力暫失中的數字表徵: 數字距離對注意力暫失的影響★ 利用跨顱磁刺激探討主動式注意力攫取的神經機制
★ 以數學模型及跨顱磁刺激探討注意力分配及眼球運動準備歷程★ 學齡前兒童之視覺注意力發展及電腦化注意力訓練效果之探討
★ 以跨顱磁刺激探討左側下部頂葉以及左側上部頂葉的功能在中文處理中所扮演的角色★ 性侵害犯的衝動行為表現-情緒狀態如何影響性侵害犯的抑制能力?
★ 學齡前階段孩童眼動抑制能力的發展和特性★ 學齡前階段孩童衝突解決和動作反應抑制能力的發展
★ 6歲孩童與成人在數字和具體數量上的自動化處理★ 期望效果之影響與可能的神經機制
★ Attentional reorienting: the dynamic interaction between goal-directed and stimulus-driven attentioinal control★ 前額葉眼動區在視覺搜尋作業上對不同干擾物特徵與顯示時間扮演的角色
★ Roles of the Pre-supplementary Motor Area and Right Inferior Frontal Gyrus in Stimulus Selective Stop-signal task: A Theta Burst Transcranial!Magnetic! Stimulation!Study★ Investigation of posterior parietal cortex visuospatial control over processing in near and far space using transcranial magnetic stimulation
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2026-7-22以後開放)
摘要(中) 視覺選擇性注意力能帮助以有限的能力處理周遭環境,而旁側抑制作業(Flanker task)是研究注意力的經典範式之一。以往研究發現腦電波(Electroencephalography, EEG)頂葉及枕葉α波(約10Hz)在視覺注意力上有抑制干擾物的作用。但是先前研究對α波扮演的作用多來自不同頻率組合間的比較,而非以典型旁側抑制作業為基準。本實驗除了採用無閃動及在目標和干擾位置上增設不同頻率閃爍的旁側抑制作業(如: 6 Hz、10 Hz 或15 Hz),觀察受到不同頻率調控後的表現,並加入目標物和干擾物均為10Hz情況作為刺激閃爍控制組,以進一步探索10Hz α波在注意力中的作用。
結果發現:
(1) 15T|10F(T跟F分別代表目標物和干擾物: 15T|10F 表示15 Hz目標物伴隨 10 Hz干擾物)下整體反應時間不僅比10T|15F短,也比10T|10T和無閃動的情況快;但6T|10F和10T|6F的對比中沒有類似情況。
(2) 10T|15F的一致性效果 (congruency effect) 造成的反應時間差異比其他的情況更長。
(3) 使用全息希爾伯特頻譜(Holo-Hilbert Spectral Analysis, HHSA)分析後發現3 ~ 4 Hz震幅調控10Hz的穩態視覺誘發電位(Steady State Visual Evoked Potential, SSVEP)強度會受注意力調控,但只在15Hz的組合中有顯著差異。
(4) 在10T|10F、10T|6F和6T|10F調控的情況,SSVEP 10Hz的強度與反應時間呈正相關。
(5) 10T|15F相較於無閃動的情況,由一致性效果造成之反應時間差異的增加量與10Hz無調控的載波的增強量正相關。
本研究透過操弄不同外在閃爍刺激引起的10Hz能量變化及其對應的行為變化,並與未受調控的情況比較。從6 Hz 和15 Hz 組合所引發的不同結果,推斷因為不同頻率的SSVEP經由不同腦區產生,α波在視覺注意力所扮演的抑制作用可能受到頻率組合的影響。本實驗中在15 Hz組合(10T|15F對比15T|10F)看到10Hz SSVEP受注意力顯著的調控,也就是局部空間的10Hz SSVEP 調控,發現對一致性效果造成影響;相反的,在6 Hz 組合中,則是影響不相容情況(incongruent condition)的反應時間。這些研究結果可增加外在閃爍刺激如何調控腦波的理解,並解析10Hz α波對注意力運作上的影響,以對未來視覺注意力實驗研究提供新的思路。
摘要(英) Visual selective attention is an important cognitive function that assists us to prioritize the processing of surrounding stimuli with a limited capacity. The flanker task is one of the conventional paradigms for investigating visual selective attention. Previous studies have found that the electroencephalography (EEG) alpha activity in the parieto-occipital lobe plays a crucial role in both visual processing and selective attention. However, due to the lack of essential baseline conditions, the entrainment effect on behavioral indexes was only inferred from the comparison of different stimulus manipulations rather than changes from the typical flanker task performance. Therefore, to decipher the role of alpha oscillation in the selective attention task, we investigated the behavioral performance as well as the corresponding EEG indexes while the participants performed a modified Eriksen flanker task, in which the stimuli were composed of target and flankers flickering at different frequencies (i.e., 10Hz, 6Hz or 15Hz), that is 10T|15F, 15T|10F, 10T|6F and 6T|10F (the flicker frequency of target and flanker position is denoted as T and F, respectively). The typical no-flicker flanker task and 10T|10F trials were also added as one of the baselines to facilitate the interpretation of the 10 Hz entrainment effect.
The results show that:
(1) The overall RT, which is calculated as the average of congruent and incongruent RT, is significantly faster in the 15T|10F condition compared to 10T|15F, as well as 10T|10F and typical flanker conditions. Similar patterns are not found in any 6 Hz combinations.
(2) The congruency effect of the 10T|15F condition is significantly faster than both 15T|10F and typical flanker conditions, but no difference is observed for 6 Hz combinations.
(3) The attentional modulation of the 3 ~ 4 Hz AM modulating 10 Hz SSVEP response decomposed by HHSA is only observed in 15 Hz but not 6 Hz combination.
(4) The stronger the 10Hz power is, the larger the incongruent RTs of the 10T|10F, 6T|10F, and 10T|6F are.
(5) The increment of the congruency effect in 10T|15F trials from the typical flanker condition of each participant can be predicted by their enhanced 10 Hz power elicited by the flicker.
Collectively, in the current study, by depicting the direct relationship between the 10 Hz power change modulated by the external flicker and behavioral performance difference between each condition and the typical no-flicker condition, we aim to elucidate the alpha role in visual selective attention. Moreover, the distinct behavioral performance and the EEG responses in different conditions, which is probably due to the frequency-specific cortical network eliciting SSVEP responses, imply that the alpha inhibitory effect may depend on tagging frequency combinations. Specifically, 3 ~ 4 Hz AM modulating 10 Hz is significantly modulated by attention for 15 Hz combinations, suggesting spatial-specific 10 Hz entrainment, and then leads to the modulation on the congruency effect. On the contrary, for 6 Hz combinations, the incongruent RTs are modulated. The present findings thus contribute to the understanding of the interaction between flicker-induced response and intrinsic oscillation and further shed new lights on the interpretation of the entrainment effect.
關鍵字(中) ★ α閃爍刺激調控
★ 穩態視覺誘發電位
★ 視覺選擇性注意力
★ 全息希爾伯特頻譜
關鍵字(英) ★ alpha entrainment
★ SSVEP
★ visual selective attention
★ HHSA
論文目次 摘 要 II
Abstract III
Acknowledgments V
Table of Contents VI
List of Figures VIII
Chapter 1 General Introduction 10
1.1 Visual selective attention 10
1.2 Steady-State Visual Evoked Potential (SSVEP) 13
1.3 Alpha Entrainment 14
1.4 Hilbert-Huang Transform Analysis (HHT) 16
1.5 Holo-Hilbert Spectrum Analysis (HHSA) 21
1.6 Research Question and Purpose 26
Chapter 2 Experimental Design and Methods 28
2.1 Experimental Design 28
2.1.1 Participants 28
2.1.2 Stimuli 28
2.1.3 Procedure 32
2.2 Methods 33
2.2.1 EEG data acquisition and data preprocessing 33
2.2.2 Data analysis and statistical analysis 34
Chapter 3 Behavioral Exploration on the Modified Flanker Task 40
3.1 Results 40
3.1.1 The 10-Hz position effect was significant for 15 Hz rather than 6 Hz tagging frequency combinations. 41
3.1.2 Comparison with the baseline conditions (i.e. no flicker trials and 10T|10F trials). 43
3.1.3 No difference is observed for the tagging frequency combination with 10Hz and 6 Hz. 44
3.2 Summary and Discussion 45
Chapter 4 Attentional Modulation of the SSVEP power 47
4.1 Results 47
4.1.1 Attentional Modulation of the grand average SSVEP power 47
4.1.2 The SSVEP response induced by the 15 Hz tagging frequency combination revealed by single-trial-based HHSA. 53
4.1.3 The SSVEP response induced by the 6 Hz tagging frequency combination revealed by single-trial-based HHSA. 55
4.1.4 The attentional modulation of the single-trial-based SSVEP revealed by HHSA. 57
4.2 Summary and Discussion 57
Chapter 5 The interplay between flicker entrainment and flanker task oscillations. 60
5.1 Results 60
5.1.1 The brain oscillation that is related to the behavioral congruency effect. 61
5.1.2 The cross-frequency coupling of the gamma oscillation is task-relevant. 62
5.1.3 The flicker induced effect to the behavioral-relevant oscillation. 68
5.2 Summary and Discussion 76
Chapter 6 Conclusion and Future Work 79
6.1 Summary of findings and discussion 79
6.2 Limitations and Future Work 87
References 90
參考文獻 Adrian, E. D., & Matthews, B. H. (1934). The Berger rhythm: potential changes from the occipital lobes in man. Brain, 57(4), 355-385.
Ales, J. M., Farzin, F., Rossion, B., & Norcia, A. M. (2012). An objective method for measuring face detection thresholds using the sweep steady-state visual evoked response. Journal of vision, 12(10), 18-18.
Appelbaum, L. G., Smith, D. V., Boehler, C. N., Chen, W. D., & Woldorff, M. G. (2011). Rapid modulation of sensory processing induced by stimulus conflict. Journal of cognitive neuroscience, 23(9), 2620-2628.
Bompas, A., Sumner, P., Muthumumaraswamy, S. D., Singh, K. D., & Gilchrist, I. D. (2015). The contribution of pre-stimulus neural oscillatory activity to spontaneous response time variability. Neuroimage, 107, 34-45. doi:10.1016/j.neuroimage.2014.11.057
Bonnefond, M., Kastner, S., & Jensen, O. (2017). Communication between brain areas based on nested oscillations. Eneuro, 4(2).
Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S., & Cohen, J. D. (1999). Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature, 402(6758), 179-181.
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological review, 108(3), 624.
Brainard, D. H., & Vision, S. (1997). The psychophysics toolbox. Spatial vision, 10(4), 433-436.
Bunge, S. A., Dudukovic, N. M., Thomason, M. E., Vaidya, C. J., & Gabrieli, J. D. (2002). Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron, 33(2), 301-311.
Bunge, S. A., Hazeltine, E., Scanlon, M. D., Rosen, A. C., & Gabrieli, J. (2002). Dissociable contributions of prefrontal and parietal cortices to response selection. Neuroimage, 17(3), 1562-1571.
Busch, N. A., Dubois, J., & VanRullen, R. (2009). The phase of ongoing EEG oscillations predicts visual perception. Journal of Neuroscience, 29(24), 7869-7876.
Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. science, 304(5679), 1926-1929.
Buzsáki, G., & Wang, X.-J. (2012). Mechanisms of gamma oscillations. Annual review of neuroscience, 35, 203-225.
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., . . . Knight, R. T. (2006). High gamma power is phase-locked to theta oscillations in human neocortex. science, 313(5793), 1626-1628.
Capilla, A., Pazo-Alvarez, P., Darriba, A., Campo, P., & Gross, J. (2011). Steady-state visual evoked potentials can be explained by temporal superposition of transient event-related responses. PLoS One, 6(1), e14543. doi:10.1371/journal.pone.0014543
Carrasco, M. (2011). Visual attention: the past 25 years. Vision Res, 51(13), 1484-1525. doi:10.1016/j.visres.2011.04.012
Cavanagh, J. F., Cohen, M. X., & Allen, J. J. (2009). Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. Journal of Neuroscience, 29(1), 98-105.
Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in cognitive sciences, 18(8), 414-421.
Chang, C. F., Liang, W. K., Lai, C. L., Hung, D. L., & Juan, C. H. (2016). Theta Oscillation Reveals the Temporal Involvement of Different Attentional Networks in Contingent Reorienting. Front Hum Neurosci, 10, 264. doi:10.3389/fnhum.2016.00264
Chorlian, D. B., Porjesz, B., & Begleiter, H. (2006). Amplitude modulation of gamma band oscillations at alpha frequency produced by photic driving. International Journal of Psychophysiology, 61(2), 262-278.
Clarke, S. E., Longtin, A., & Maler, L. (2015). Contrast coding in the electrosensory system: parallels with visual computation. Nat Rev Neurosci, 16(12), 733-744. doi:10.1038/nrn4037
Clayton, M. S., Yeung, N., & Cohen Kadosh, R. (2018). The many characters of visual alpha oscillations. European Journal of Neuroscience, 48(7), 2498-2508.
Cohen, M. X., & Cavanagh, J. F. (2011). Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict. Frontiers in psychology, 2, 30.
Colominas, M. A., Schlotthauer, G., & Torres, M. E. (2014). Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control, 14, 19-29. doi:10.1016/j.bspc.2014.06.009
Colominas, M. A., Schlotthauer, G., Torres, M. E., & Flandrin, P. (2013). Noise-Assisted Emd Methods in Action. Advances in Adaptive Data Analysis, 04(04). doi:10.1142/s1793536912500252
Cottereau, B., Lorenceau, J., Gramfort, A., Clerc, M., Thirion, B., & Baillet, S. (2011). Phase delays within visual cortex shape the response to steady-state visual stimulation. Neuroimage, 54(3), 1919-1929. doi:10.1016/j.neuroimage.2010.10.004
de Graaf, T. A., Gross, J., Paterson, G., Rusch, T., Sack, A. T., & Thut, G. (2013). Alpha-band rhythms in visual task performance: phase-locking by rhythmic sensory stimulation. PLoS One, 8(3), e60035. doi:10.1371/journal.pone.0060035
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21.
Desimone, R. (1998). Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 353(1373), 1245-1255.
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual review of neuroscience, 18(1), 193-222.
Di Russo, F., Pitzalis, S., Aprile, T., Spitoni, G., Patria, F., Stella, A., . . . Hillyard, S. A. (2007). Spatiotemporal analysis of the cortical sources of the steady-state visual evoked potential. Hum Brain Mapp, 28(4), 323-334. doi:10.1002/hbm.20276
Ding, J., Sperling, G., & Srinivasan, R. (2006). Attentional modulation of SSVEP power depends on the network tagged by the flicker frequency. Cereb Cortex, 16(7), 1016-1029. doi:10.1093/cercor/bhj044
Dockree, P. M., Kelly, S. P., Foxe, J. J., Reilly, R. B., & Robertson, I. H. (2007). Optimal sustained attention is linked to the spectral content of background EEG activity: greater ongoing tonic alpha (approximately 10 Hz) power supports successful phasic goal activation. Eur J Neurosci, 25(3), 900-907. doi:10.1111/j.1460-9568.2007.05324.x
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & psychophysics, 16(1), 143-149.
Fassbender, C., Simoes-Franklin, C., Murphy, K., Hester, R., Meaney, J., Robertson, I., & Garavan, H. (2006). The role of a right fronto-parietal network in cognitive control: Common activations for" cues-to-attend" and response inhibition. Journal of Psychophysiology, 20(4), 286.
Foxe, J. J., Simpson, G. V., & Ahlfors, S. P. (1998). Parieto-occipital∼ 1 0Hz activity reflects anticipatory state of visual attention mechanisms. Neuroreport, 9(17), 3929-3933.
Foxe, J. J., & Snyder, A. C. (2011). The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention. Front Psychol, 2, 154. doi:10.3389/fpsyg.2011.00154
Friese, U., Köster, M., Hassler, U., Martens, U., Trujillo-Barreto, N., & Gruber, T. (2013). Successful memory encoding is associated with increased cross-frequency coupling between frontal theta and posterior gamma oscillations in human scalp-recorded EEG. Neuroimage, 66, 642-647.
Gulbinaite, R., Roozendaal, D. H. M., & VanRullen, R. (2019). Attention differentially modulates the amplitude of resonance frequencies in the visual cortex. Neuroimage, 203, 116146. doi:10.1016/j.neuroimage.2019.116146
Gulbinaite, R., van Rijn, H., & Cohen, M. X. (2014). Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control. Frontiers in human neuroscience, 8, 761.
Gulbinaite, R., van Viegen, T., Wieling, M., Cohen, M. X., & VanRullen, R. (2017). Individual Alpha Peak Frequency Predicts 10 Hz Flicker Effects on Selective Attention. J Neurosci, 37(42), 10173-10184. doi:10.1523/JNEUROSCI.1163-17.2017
Hanslmayr, S., Pastötter, B., Bäuml, K.-H., Gruber, S., Wimber, M., & Klimesch, W. (2008). The electrophysiological dynamics of interference during the Stroop task. Journal of cognitive neuroscience, 20(2), 215-225.
Hazeltine, E., Poldrack, R., & Gabrieli, J. D. (2000). Neural activation during response competition. Journal of cognitive neuroscience, 12(Supplement 2), 118-129.
Herrmann, C. S. (2001). Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena. Experimental brain research, 137(3), 346-353.
Hillyard, S. A., & Anllo-Vento, L. (1998). Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences, 95(3), 781-787.
Huang, N. E., Hu, K., Yang, A. C., Chang, H.-C., Jia, D., Liang, W.-K., . . . Peng, C. K. (2016). On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150206.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., . . . Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On instantaneous frequency. Advances in Adaptive Data Analysis, 1(02), 177-229.
Hyafil, A., Giraud, A.-L., Fontolan, L., & Gutkin, B. (2015). Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends in neurosciences, 38(11), 725-740.
James, W. (1890). The principles of psychology (Vol. 1): Henry Holt and Company, United States.
Janssens, C., De Loof, E., Boehler, C. N., Pourtois, G., & Verguts, T. (2018). Occipital alpha power reveals fast attentional inhibition of incongruent distractors. Psychophysiology, 55(3). doi:10.1111/psyp.13011
Jensen, O., & Mazaheri, A. (2010). Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci, 4, 186. doi:10.3389/fnhum.2010.00186
Jones, S. R., Kerr, C. E., Wan, Q., Pritchett, D. L., Hämäläinen, M., & Moore, C. I. (2010). Cued spatial attention drives functionally relevant modulation of the mu rhythm in primary somatosensory cortex. Journal of Neuroscience, 30(41), 13760-13765.
Köster, M., Martens, U., & Gruber, T. (2019). Memory entrainment by visually evoked theta-gamma coupling. Neuroimage, 188, 181-187.
Keitel, C., Quigley, C., & Ruhnau, P. (2014). Stimulus-driven brain oscillations in the alpha range: entrainment of intrinsic rhythms or frequency-following response? J Neurosci, 34(31), 10137-10140. doi:10.1523/JNEUROSCI.1904-14.2014
Kleiner, M., Brainard, D., & Pelli, D. (2007). What′s new in Psychtoolbox-3?
Klimesch, W. (2012). α-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci, 16(12), 606-617. doi:10.1016/j.tics.2012.10.007
Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: the inhibition-timing hypothesis. Brain Res Rev, 53(1), 63-88. doi:10.1016/j.brainresrev.2006.06.003
Krahe, R., & Maler, L. (2014). Neural maps in the electrosensory system of weakly electric fish. Current opinion in neurobiology, 24, 13-21.
Larson, M. J., Kaufman, D. A., & Perlstein, W. M. (2009). Neural time course of conflict adaptation effects on the Stroop task. Neuropsychologia, 47(3), 663-670.
Lin, F.-C., Zao, J. K., Tu, K.-C., Wang, Y., Huang, Y.-P., Chuang, C.-W., . . . Jung, T.-P. (2012). SNR analysis of high-frequency steady-state visual evoked potentials from the foveal and extrafoveal regions of human retina. Paper presented at the 2012 Annual international conference of the IEEE engineering in medicine and biology society.
Liotti, M., Woldorff, M. G., Perez III, R., & Mayberg, H. S. (2000). An ERP study of the temporal course of the Stroop color-word interference effect. Neuropsychologia, 38(5), 701-711.
Lisman, J. E., & Jensen, O. (2013). The theta-gamma neural code. Neuron, 77(6), 1002-1016.
Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods, 164(1), 177-190.
Mathewson, K. E., Fabiani, M., Gratton, G., Beck, D. M., & Lleras, A. (2010). Rescuing stimuli from invisibility: Inducing a momentary release from visual masking with pre-target entrainment. Cognition, 115(1), 186-191. doi:10.1016/j.cognition.2009.11.010
Mathewson, K. E., Gratton, G., Fabiani, M., Beck, D. M., & Ro, T. (2009). To see or not to see: prestimulus alpha phase predicts visual awareness. J Neurosci, 29(9), 2725-2732. doi:10.1523/jneurosci.3963-08.2009
Mathewson, K. E., Prudhomme, C., Fabiani, M., Beck, D. M., Lleras, A., & Gratton, G. (2012). Making waves in the stream of consciousness: entraining oscillations in EEG alpha and fluctuations in visual awareness with rhythmic visual stimulation. J Cogn Neurosci, 24(12), 2321-2333. doi:10.1162/jocn_a_00288
McDermott, T. J., Wiesman, A. I., Proskovec, A. L., Heinrichs-Graham, E., & Wilson, T. W. (2017). Spatiotemporal oscillatory dynamics of visual selective attention during a flanker task. Neuroimage, 156, 277-285. doi:10.1016/j.neuroimage.2017.05.014
Morgan, S., Hansen, J., & Hillyard, S. (1996). Selective attention to stimulus location modulates the steady-state visual evoked potential. Proceedings of the National Academy of Sciences, 93(10), 4770-4774.
Nguyen, K. T., Liang, W.-K., Lee, V., Chang, W.-S., Muggleton, N. G., Yeh, J.-R., . . . Juan, C.-H. (2019). Unraveling nonlinear electrophysiologic processes in the human visual system with full dimension spectral analysis. Scientific Reports, 9(1), 16919. doi:10.1038/s41598-019-53286-z
Nguyen, T. V., Balachandran, P., Muggleton, N. G., Liang, W.-K., & Juan, C.-H. (2021). Dynamical EEG Indices of Progressive Motor Inhibition and Error-Monitoring. Brain Sciences, 11(4). doi:10.3390/brainsci11040478
Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015). The steady-state visual evoked potential in vision research: A review. J Vis, 15(6), 4. doi:10.1167/15.6.4
Osipova, D., Hermes, D., & Jensen, O. (2008). Gamma power is phase-locked to posterior alpha activity. PLoS One, 3(12), e3990.
Padrão, G., Rodriguez-Herreros, B., Zapata, L. P., & Rodriguez-Fornells, A. (2015). Exogenous capture of medial–frontal oscillatory mechanisms by unattended conflicting information. Neuropsychologia, 75, 458-468.
Pastötter, B., Dreisbach, G., & Bäuml, K.-H. T. (2013). Dynamic adjustments of cognitive control: oscillatory correlates of the conflict adaptation effect. Journal of cognitive neuroscience, 25(12), 2167-2178.
Pelli, D. G., & Vision, S. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial vision, 10, 437-442.
Popov, T., Kastner, S., & Jensen, O. (2017). FEF-Controlled Alpha Delay Activity Precedes Stimulus-Induced Gamma-Band Activity in Visual Cortex. J Neurosci, 37(15), 4117-4127. doi:10.1523/JNEUROSCI.3015-16.2017
Posner, M. I., & Cohen, Y. (1984). Components of visual orienting. Attention and performance X: Control of language processes, 32, 531-556.
Regan, D. (1977). Steady-state evoked potentials. JOSA, 67(11), 1475-1489.
Regan, D. (1989). Human brain electrophysiology. Evoked potentials and evoked magnetic fields in science and medicine.
Regan, D., & Cartwright, R. (1970). A method of measuring the potentials evoked by simultaneous stimulation of different retinal regions. Electroencephalography and clinical neurophysiology, 28(3), 314-319.
Sadaghiani, S., & Kleinschmidt, A. (2016). Brain Networks and α-Oscillations: Structural and Functional Foundations of Cognitive Control. Trends Cogn Sci, 20(11), 805-817. doi:10.1016/j.tics.2016.09.004
Schack, B., Vath, N., Petsche, H., Geissler, H.-G., & Möller, E. (2002). Phase-coupling of theta–gamma EEG rhythms during short-term memory processing. International Journal of Psychophysiology, 44(2), 143-163.
Schwartz, O., & Simoncelli, E. P. (2001). Natural signal statistics and sensory gain control. Nature neuroscience, 4(8), 819-825.
Shapley, R. (1998). Visual cortex: pushing the envelope. Nature neuroscience, 1(2), 95-96.
Snyder, A. C., & Foxe, J. J. (2010). Anticipatory attentional suppression of visual features indexed by oscillatory alpha-band power increases: a high-density electrical mapping study. Journal of Neuroscience, 30(11), 4024-4032.
Spaak, E., de Lange, F. P., & Jensen, O. (2014). Local entrainment of alpha oscillations by visual stimuli causes cyclic modulation of perception. J Neurosci, 34(10), 3536-3544. doi:10.1523/JNEUROSCI.4385-13.2014
Srinivasan, R., Bibi, F. A., & Nunez, P. L. (2006). Steady-state visual evoked potentials: distributed local sources and wave-like dynamics are sensitive to flicker frequency. Brain Topogr, 18(3), 167-187. doi:10.1007/s10548-006-0267-4
Stam, C. J. (2005). Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clinical neurophysiology, 116(10), 2266-2301.
Tsai, C. C., & Liang, W. K. (2021). Event-related components are structurally represented by intrinsic event-related potentials. Scientific reports, 11(1), 1-14.
Thut, G., Schyns, P. G., & Gross, J. (2011). Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Front Psychol, 2, 170. doi:10.3389/fpsyg.2011.00170
Tononi, G., Srinivasan, R., Russell, D. P., & Edelman, G. M. (1998 ). Investigating neural correlates of conscious perception by frequency-tagged neuromagnetic responses.pdf. Proceedings of the National Academy of Sciences, 95(6), 3198-3203. doi:10.1073/pnas.95.6.3198
Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. Paper presented at the 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP).
van Diepen, R. M., Cohen, M. X., Denys, D., & Mazaheri, A. (2015). Attention and temporal expectations modulate power, not phase, of ongoing alpha oscillations. J Cogn Neurosci, 27(8), 1573-1586. doi:10.1162/jocn_a_00803
van Dijk, H., Schoffelen, J. M., Oostenveld, R., & Jensen, O. (2008). Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability. J Neurosci, 28(8), 1816-1823. doi:10.1523/JNEUROSCI.1853-07.2008
van Veen, V., & Carter, C. S. (2002). The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiol Behav, 77(4-5), 477-482. doi:10.1016/s0031-9384(02)00930-7
Van Veen, V., Cohen, J. D., Botvinick, M. M., Stenger, V. A., & Carter, C. S. (2001). Anterior cingulate cortex, conflict monitoring, and levels of processing. Neuroimage, 14(6), 1302-1308.
Vialatte, F. B., Maurice, M., Dauwels, J., & Cichocki, A. (2010). Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog Neurobiol, 90(4), 418-438. doi:10.1016/j.pneurobio.2009.11.005
Wendt, M., Heldmann, M., Münte, T. F., & Kluwe, R. H. (2007). Disentangling sequential effects of stimulus-and response-related conflict and stimulus-response repetition using brain potentials. Journal of cognitive neuroscience, 19(7), 1104-1112.
Wiesman, A. I., Groff, B. R., & Wilson, T. W. (2019). Frontoparietal Networks Mediate the Behavioral Impact of Alpha Inhibition in Visual Cortex. Cereb Cortex, 29(8), 3505-3513. doi:10.1093/cercor/bhy220
Wiesman, A. I., & Wilson, T. W. (2019). Alpha Frequency Entrainment Reduces the Effect of Visual Distractors. J Cogn Neurosci, 31(9), 1392-1403. doi:10.1162/jocn_a_01422
Williams, N., Nasuto, S. J., & Saddy, J. D. (2011). Evaluation of empirical mode decomposition for event-related potential analysis. EURASIP Journal on Advances in Signal Processing, 2011, 1-11.
Worden, M. S., Foxe, J. J., Wang, N., & Simpson, G. V. (2000). Anticipatory biasing of visuospatial attention indexed by retinotopically specific α-bank electroencephalography increases over occipital cortex. Journal of Neuroscience, 20(6), RC63-RC63.
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(01), 1-41.
指導教授 阮啟弘(Chi-Hung Juan) 審核日期 2021-7-22
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明