| Popis: |
The electroencephalogram (EEG) is the record of cerebral electrophysiological activity along the scalp in which it has been widely used in several applications, for example, the quantification of the cognitive capacity focusing on the diagnosis or even Brain Machine Interfaces (BCI). However, neural data are frequently contaminated by artifacts that may originate from recording devices or by non-brain physiological activities, such as the blink and the contraction of the scalp muscles. This last represents a considerable challenge in the removal of EEG artifacts due to high amplitude and broad frequency range, which makes it difficult to remove by conventional filtering. Thus, some applications such as BCI systems may occasionally be associated with frequent contractions of head muscles corrupting the EEG control signal. This requires the application of a number of filtering techniques. However, the standard gold techniques still contain limitations, such as not completely eliminating noise in all EEG channels. For this reason, besides to the study of the techniques is helpful to understand the electromyography (EMG) contamination along the scalp. The present work aims to quantitatively estimate EMG noise in 16 bipolar channels of EEG distributed along the scalp according to the 10–20 system. This estimation was based on experimental protocol considering the simultaneous acquisition of EEG and EMG of the frontal muscle sampled at 5 kHz. The protocol consisted of 15 beeps, while listening to these beeps the volunteer made a contraction of the frontal muscle. The mean power of the EEG contaminated by EMG of frontal muscle contraction was compare between the periods of muscle contraction and non-contraction. The results show a mean increase of approximately 74.9% in the power of EEG signals during the muscular contraction. Thus, we conclude that frontal muscular contamination is present on the all EEG rhythms and all over the scalp. |
