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首页> 外文期刊>Sport sciences for health >Familiarity affects electrocortical power spectra during dance imagery, listening to different music genres: independent component analysis of Alpha and Beta rhythms
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Familiarity affects electrocortical power spectra during dance imagery, listening to different music genres: independent component analysis of Alpha and Beta rhythms

机译：熟悉程度影响舞蹈图像期间的电蚀刻，听取不同的音乐类型：α和β节奏的独立分量分析

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Background Changes in electrocortical activity during motor imagery are among the most interesting findings in the recent neuroscientific studies; existing studies, however, do not focus specifically on Alpha and Beta rhythms, in relationship with the motor experience and verifying if the style of music can influence the cortical activity during motor imagery task. Power spectra analysis was used to compare the EEG activity during dance imagery tasks in a group of dancers and in a control group. Methods Twenty-one volunteers performed a dance imagery task listening to three kinds of music. For EEG acquisition an 8-channel headset with wireless amplifier was used. This study used independent component analysis (ICA) to assess EEG frequency spectrum associated with the musical genre. High Alpha (10.0-11.5 Hz) and Low Beta (12.0-15.5 Hz) EEG frequency spectra were analyzed. Considering EEG power spectra analysis, no statistically significant differences were found between groups at baseline. Results Statistically significant difference (p<0.01) emerged comparingthe two groups, during dance imagery task, listening to Classical, Rock and Waltz music, in High Alpha band; whereas listening to Classical music in Low Beta band. Expert group showed a greater power level with respect to control group for both bands. No statistically significant difference emerged in EEG power spectrum, comparing the three kinds of music, for both groups (intra-group analysis). Findings are comparable with previous studies obtained by other neuroimaging modalities, highlighting how high-resolution EEG may prove to be a promising tool for measuring cortex electrical activity during motor imagery. This study successfully applied ICA to decompose the EEG segments recorded during the tasks, finding consistent independent brain processes across multiple subjects. Conclusions The statistically significant differences between expert dancers and controls, could indicate a difference in the attentional effort during the dance imagery task. The remarks partially confirm existing findings on the relationship among EEG activity, Alpha rhythm and motor imagery and also extend the knowledge on the EEG response to auditory stimuli during motor imagery in particular for Beta rhythm components, emphasizing specific characteristics in function of the level of familiarity to the dance imagery task
机译：电动成像期间电离活动的背景变化是最近神经科学研究中最有趣的结果;然而，现有的研究不会专门关注alpha和beta节奏，与电机经验的关系，并验证音乐风格是否可以影响电动机图像任务期间的皮质活动。功率谱分析用于比较一组舞者和对照组的舞蹈图像任务期间的EEG活动。方法二十一名志愿者进行了舞蹈图像任务，听着三种音乐。对于EEG获取，使用带有无线放大器的8通道耳机。本研究使用独立分量分析（ICA）来评估与音乐类型相关的脑电图频谱。分析了高α（10.0-11.5Hz）和低β（12.0-15.5Hz）脑电图频谱。考虑到EEG功率谱分析，基线组之间没有发现统计学上显着的差异。结果统计上显着差异（P <0.01）出现比较两组，在舞蹈图像任务期间，在高alpha乐队中听古典，摇滚乐和华尔兹音乐;而在低测试乐队中听古典音乐。专家组在两个频段的控制组方面表现出更大的功率水平。 EEG功率谱中没有出现统计学意义的差异，比较三种音乐，两个群体（间分析内）。结果与其他神经影像模型获得的先前研究相当，突出了高分辨率EEG如何证明在电动机图像中测量皮质电活动的有希望的工具。本研究成功应用了ICA以分解在任务期间记录的脑电图段，在多个受试者中找到一致的独立大脑过程。结论专家舞者与控制之间的统计学意义差异，可以表明舞蹈图像任务期间的注意力努力。该备注部分确认了EEG活动，alpha节奏和电动机图像之间关系的现有结果，并且还延长了对β节奏组分的电动机图像中对听觉刺激的EEG反应的知识，特别是熟悉熟悉程度的特定特征舞蹈图像任务

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