Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies

Abstract

EEG-neurofeedback (NFB) became a very popular method aimed at improving cognitive and behavioral performance. However, the EMG frequency spectrum overlies the higher EEG oscillations and the NFB trainings focusing on these frequencies is hindered by the problem of EMG load in the information fed back to the subjects. In such a complex signal, it is highly probable that the most controllable component will form the basis for operant conditioning. This might cause different effects in the case of various training protocols and therefore needs to be carefully assessed before designing training protocols and algorithms. In the current experiment a group of healthy adults (n = 14) was trained by professional trainers to up-regulate their beta1 (15–22 Hz) band for eight sessions. The control group (n = 18) underwent the same training regime but without rewards for increasing beta. In half of the participants trained to up-regulate beta1 band (n = 7) a systematic increase in tonic EMG activity was identified offline, implying that muscle activity became a foundation for reinforcement in the trainings. The remaining participants did not present any specific increase of the trained beta1 band amplitude. The training was perceived effective by both trainers and the trainees in all groups. These results indicate the necessity of proper control of muscle activity as a requirement for the genuine EEG-NFB training, especially in protocols that do not aim at the participants’ relaxation. The specificity of the information fed back to the participants should be of highest interest to all therapists and researchers, as it might irreversibly alter the results of the training.

The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG

“…Eight methods derive from the Cartesian product of the following two parameters: method (RP or RPF) and update mode (dynamic, semi-dynamic initialized on assessment (the subject's first available recording), semidynamic initialized on all other subjects (using a generic calibration), and static initialized on assessment). These Riemannian techniques are compared to another online method called Artifact Subspace Reconstruction (ASR) [66], originally designed for artifact correction, but which can be easily used for artifact detection 7 and can be considered as online static 8 .…”

“…Consequently, measuring signal quality and denoising artifacted epochs are crucial steps for any application relying on EEG data analysis [3], [4]. This is specially important for real-time applications, such as BCI or NFB, where the system behavior fully depends on the current data quality and the presence of artifacts can disturb the feedback stream [5]- [7]. In this context, the task is significantly more complex: it cannot be done retrospectively as in offline analysis, by a human operator or an automatic offline algorithm; denoising can only rely on automated pre-calibrated approaches that should be more generalizable as compared to an offline strategy.…”

Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback

“…Demographic, physiological or psychological factors had not been much investigated [87], but there is some evidence that the feeling of being able to control technological devices affects the performance [92], as makes the choice of mental strategy during training [93]. Furthermore, Paluch et al have discovered that subjects who train at high-frequencies often learn to control muscle activity instead of brain activity [94]. Since muscle activity can easily disturb the EEG signal, the training can be perceived as successful whereas, in reality, the subject does not modulate brain activity.…”

EEG Characterization During Motor Tasks That Are Difficult for Movement Disorder Patients

“…Still, researchers debate whether gamma oscillations are directly linked to these functions or if they are just an epiphenomenon or byproduct of other waveforms [58]. In this frequency band, extra caution should be taken when drawing results, as EMG is sensitive to cognitive processes [59,60]. Further, temporal, occipitofrontal, and auricular muscles on the head can produce signals in the gamma band that constitute the majority of the total gamma power [60].…”

“…In this frequency band, extra caution should be taken when drawing results, as EMG is sensitive to cognitive processes [59,60]. Further, temporal, occipitofrontal, and auricular muscles on the head can produce signals in the gamma band that constitute the majority of the total gamma power [60].…”