心算任务复杂度对脑电theta,alpha和beta波的影响
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摘要
目的以1Hz为频宽单位比较两种复杂度心算任务下4～30Hz范围的脑电功率谱,分析其与文献中以经典脑电波段划分所得结果的关系。方法14名健康志愿者完成两种复杂度的心算任务,同步采集其脑电数据。对前额和顶叶区域内10个代表性通道预处理后的脑电数据采用Welch算法实现谱估计,相对基线期做归一化处理,获得两种复杂度心算任务下的相对功率谱密度,并逐一配对t检验每1Hz频宽单位内功率谱差异的显著性。结果前额和顶叶区域能体现心算任务复杂度差异的脑电信号精细频率范围分别为10～25Hz和9～25Hz(P<0.05)。结论较高频段alpha波和较低频段beta波能较好地表征心算任务的复杂度,以1Hz为频宽单位对脑电功率谱的精细区间分析是对基于经典脑电节律划分分析的一种补充。
Objective Using 1 Hz as the unit of bandwidth,the electrocardiogram(EEG)power spectrum estimates within the 4～30 Hz frequency range of two complexity levels of mental arithmetic(MA)tasks were compared and its relationship with the results in literature using classic EEG band subdivision method was analyzed.Methods Multichannel EEGs were recorded in 14 healthy subjects performing two complexity levels of MA tasks.The Welch power spectral estimation algorithm and the baseline normalization were employed for the preprocessed EEG data of 10 representative electrodes in the prefrontal and parietal areas so as to obtain the relative power spectra density(rPSD).Paired t-tests were performed on rPSD for two complexity levels of MA tasks at each 1 Hz frequency step.Results There were significant differences in EEG prefrontal and parietal rPSD of two complexity levels of MA tasks within the 10～25 Hz and 9～25 Hz frequency ranges respectively(P<0.05).Conclusion The rPSD in the upper alpha and lower beta bands are expected to be used as indicators for characterizing mental arithmetic task complexity.The analysis of EEG power spectrum using 1 Hz as the unit of bandwidth is a complement to that based on the classical EEG rhythm subdivision.
Objective Using 1 Hz as the unit of bandwidth,the electrocardiogram(EEG)power spectrum estimates within the 4～30 Hz frequency range of two complexity levels of mental arithmetic(MA)tasks were compared and its relationship with the results in literature using classic EEG band subdivision method was analyzed.Methods Multichannel EEGs were recorded in 14 healthy subjects performing two complexity levels of MA tasks.The Welch power spectral estimation algorithm and the baseline normalization were employed for the preprocessed EEG data of 10 representative electrodes in the prefrontal and parietal areas so as to obtain the relative power spectra density(rPSD).Paired t-tests were performed on rPSD for two complexity levels of MA tasks at each 1 Hz frequency step.Results There were significant differences in EEG prefrontal and parietal rPSD of two complexity levels of MA tasks within the 10～25 Hz and 9～25 Hz frequency ranges respectively(P<0.05).Conclusion The rPSD in the upper alpha and lower beta bands are expected to be used as indicators for characterizing mental arithmetic task complexity.The analysis of EEG power spectrum using 1 Hz as the unit of bandwidth is a complement to that based on the classical EEG rhythm subdivision.
引文
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[41] Wingeier B,Tcheng T,Koop MM,et al.Intra-operative STN DBS attenuates the prominent beta rhythm in the STN in Parkinson’s disease[J].Experimental Neurology,2006,197(1):244-251.
[42] Kühn AA,Kempf F,Brücke C,et al.High-frequency stimulation of the subthalamic nucleus suppresses oscillatory beta activity in patients with Parkinson’s disease in parallel with improvement in motor performance[J].Journal of Neuroscience,2008,28(24):6165-6173.
[43] Engel AK,Fries P.Beta-band oscillations-signaling the status quo?[J].Current Opinion in Neurobiology,2010,20(2):156-165.
[2] Groen GJ,Parkman JM.A chronometric analysis of simple addition[J].Psychological Review,1972,79(4):329-343.
[3] Dedovic K,Renwick R,Mahani NK,et al.The montreal imaging stress task:Using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain[J].Journal of Psychiatry&Neuroscience,2005,30(5):319-325.
[4] Baddeley A.Working memory:Looking back and looking forward[J].Nature Reviews Neuroscience,2003,4(10):829-839.
[5] Kazui H,Kitagaki H,Mori E.Cortical activation during retrieval of arithmetical facts and actual calculation:A functional magnetic resonance imaging study[J].Psychiatry Clin.Neurosci,2000,54(1):479-485.
[6] Lisman JE,Jensen O.The theta-gamma neural code[J].Neuron,2013,77(6):1002-1016.
[7] Klimesch W.EEG alpha and theta oscillations reflect cognitive and memory performance:A review and analysis,brain research[J].Brain Res Rev,1999,29(2-3):169-195.
[8] Ishii R,Canuet L,Ishihara T,et al.Frontal midline theta rhythm and gamma power changes during focused attention on mental calculation:An MEG beamformer analysis[J].Frontiers in Human Neuroscience,2014,8:406.
[9] Siegel M,Donner TH,Oostenveld R,et al.Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention[J].Neuron,2008,60(4):709-719.
[10] Lalo E,Gilbertson T,Doyle L,et al.Phasic increases in cortical beta activity are associated with alterations in sensory processing in the human[J].Experimental Brain Research,2007,177(1):137-145.
[11] Sammer G,Blecker C,Gebhardt H,et al.Relationship between regional hemodynamic activity and simultaneously recorded EEG-theta associated with mental arithmetic-induced workload[J].Human Brain Mapping,2010,28(8):793-803.
[12] Micheloyannis S,Sakkalis V,Vourkas M,et al.Neural networks involved in mathematical thinking:evidence from linear and non-linear analysis of electroencephalographic activity[J].Neuroscience Letters,2005,373(3):212-217.
[13] Grtner M,Grimm S,Bajbouj M.Frontal midline theta oscillations during mental arithmetic:Effects of stress[J].Frontiers in Behavioral Neuroscience,2015,9:96.
[14] Alonso JF,Romero S,Ballester MR,et al.Stress assessment based on EEG univariate features and functional connectivity measures[J].Physiological Measurement,2015,36(7):1351-1365.
[15] Al-Shargie F,Kiguchi M,Badruddin N,et al.Mental stress assessment using simultaneous measurement of EEG and fNIRS[J].Biomedical Optics Express,2016,7(10):3882-3898.
[16] Trübutschek D,Marti S,Ojeda A,et al.A theory of working memory without consciousness or sustained activity[J].eLife,2017,6:e23871.
[17]刘昌.心算加工的认知神经科学研究[J].心理科学,2006,29(1):30-33.Liu C.Mental arithmetic:Studies cognitive neuroscience[J].Psychological Science,2006,29(1):30-33.
[18] Delorme A,Makeig S.EEGLAB:An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J].Journal of Neuroscience Methods,2004,134(1):9-21.
[19] Bell,AJ,Sejnowski, TJ.An information-maximization approach to blind separation and blind deconvolution[J].Neural Computation,1995,7(6):1129-1159.
[20] Hyvrinen A,Oja E.A fast fixed-point algorithm for independent component analysis[J].Neural computation,1997,9(7):1483-1492.
[21]邱天爽,唐洪,刘海龙.统计信号处理:医学信号分析与处理[M].北京:科学出版社,2012:123-124.Qiu TS,Tang H,Liu HL.Statistical Signal Processing:Medical Signal Analysis and Processing[M].Beijing:Science Press,2012:123-124.
[22] Cohen MX.Analyzing Neural Time Series Data:Theory and Practice[M].Cambridge,Massachusetts:Massachusetts Institute of Technology,2014:220-222.
[23] Fink A,Grabner RH,Neuper C,et al.EEG alpha band dissociation with increasing task demands[J].Cognitive Brain Research,2005,24(2):252-259.
[24] Gevins A,Smith ME,Leong H,et al.Monitoring working memory load during computer-based tasks with EEG pattern recognition methods[J].Human Factors,1998,40(1):79-91.
[25] Gevins A,Smith ME.Neurophysiological measures of cognitive workload during human-computer interaction[J].Theoretical Issues in Ergonomics Science,2003,4(1-2):113-131.
[26] Cavanagh JF,Frank MJ.Frontal theta as a mechanism for cognitive control[J].Trends in Cognitive Sciences,2014,18(8):414-421.
[27]窦萌萌,田心,张丹,等.健康人工作记忆脑电theta网络因果流研究[J].航天医学与医学工程,2017,30(3):198-202.Dou MM,Tian X,Zhang D,et al.Causal flow of EEGs theta network during visual working memory task in healthy subjects[J].Space Medicine&Medical Engineering,2017,30(3):198-202.
[28]王禹,肖毅,周前祥,等.基于脑电信号的脑力负荷监测技术研究现状[J].航天医学与医学工程,2018,31(5):93-98.Wang Y,Xiao Y,Zhou QX,et al.Research progress of mental workload monitoring technology based on EEG[J].Space Medicine&Medical Engineering,2018,31(5):577-582.
[29] Logie RH,Gilhooly KJ.Counting on working memory in arithmetic problem solving[J].Memory&Cognition,1994,22(4):395-410.
[30] Gaertner M,Rohde-Liebenau L,Grimm S,et al.Working memory-related frontal theta activity is decreased under acute stress[J].Psychoneuroendocrinology,2014,43:105-113.
[31] Qin SZ,Hermans EJ,Marle HJFV,et al.Acute psychological stress reduces working memory-related activity in the dorsolateral prefrontal cortex[J].Biological Psychiatry,2009,66(1):25-32.
[32] Gevins A,Smith ME,Mcevoy L,et al.High-resolution EEG mapping of cortical activation related to working memory:Effects of task difficulty,type of processing,and practice.[J].Cerebral Cortex,1997,7(4):374-385.
[33] Pfurtscheller G.Event-related synchronization(ERS):An electrophysiological correlate of cortical areas at rest[J].Electro-encephalography&Clinical Neuro-physiology,1992,83(1):62-69.
[34] Ewing KC,Fairclough SH,Gilleade K.Evaluation of an adaptive game that uses EEG measures validated during the design process as inputs to a biocybernetic loop[J].Frontiers in Human Neuroscience,2016,10:223.
[35]徐凤刚,韩东旭,肖毅,等.追踪任务下脑波频率特征的变化特点研究[J].航天医学与医学工程,2017,30(5):333-340.Xu FG,Han DX,Xiao Y,et al.Changes of EEG frequency during different difficulty tracking tasks[J].Space Medicine&Medical Engineering,2017,30(5):333-340.
[36] Schlink BR,Peterson SM,Hairston WD,et al.Independent component analysis and source localization on mobile EEG data can identify increased levels of acute stress[J].Frontiers in Human Neuroscience,2017,11:310.
[37] Donner TH,Siegel M,Fries P,et al.Buildup of choicepredictive activit y in human motor cortex during perceptual decision making[J].Current Biology Cb,2009,19(18):1581-1585.
[38] Pogosyan A,Gaynor LD,Eusebio A,et al.Boosting cortical activity at beta-band frequencies slows movement in humans[J].Current Biology,2009,19(19):1637-1641.
[39] Buschman TJ,Miller EK.Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices[J].Science,2007,315(5820):1860-1862.
[40] Von Stein A,Chiang C,Konig P.Top-down processing mediated by interareal synchronization[J].Proc Natl Acad Sci,2000,97(26):14748-14753.
[41] Wingeier B,Tcheng T,Koop MM,et al.Intra-operative STN DBS attenuates the prominent beta rhythm in the STN in Parkinson’s disease[J].Experimental Neurology,2006,197(1):244-251.
[42] Kühn AA,Kempf F,Brücke C,et al.High-frequency stimulation of the subthalamic nucleus suppresses oscillatory beta activity in patients with Parkinson’s disease in parallel with improvement in motor performance[J].Journal of Neuroscience,2008,28(24):6165-6173.
[43] Engel AK,Fries P.Beta-band oscillations-signaling the status quo?[J].Current Opinion in Neurobiology,2010,20(2):156-165.