Closed-Loop Frontal Midlineθ Neurofeedback: A Novel Approach for Training Focused-Attention Meditation

Tracy Brandmeyer, Arnaud Delorme, Tracy Brandmeyer, Arnaud Delorme

Abstract

Cortical oscillations serve as an index of both sensory and cognitive processes and represent one of the most promising candidates for training and targeting the top-down mechanisms underlying executive functions. Research findings suggest that theta (θ) oscillations (3-7 Hz) recorded over frontal-midline electrodes are broadly associated with a number of higher-order cognitive processes and may serve as the mechanistic backbone for cognitive control. Frontal-midline theta (FMθ) oscillations have also been shown to inversely correlate with activity in the default mode network (DMN), a network in the brain linked to spontaneous thought processes such as mind-wandering and rumination. In line with these findings, we previously observed increased FMθ oscillations in expert meditation practitioners during reported periods of focused-attention meditation practice when compared to periods of mind-wandering. In an effort to narrow the explanatory gap by directly connecting observed neurophysiological activity in the brain to the phenomenological nature of reported experience, we designed a methodologically novel and adaptive neurofeedback protocol with the aim of modulating FMθ while having meditation novice participants implement breath-focus strategies derived from focused-attention mediation practices. Participants who received eight sessions of the adaptive FMθ-meditation neurofeedback protocol were able to significantly modulate FMθ over frontal electrodes using focused-attention meditation strategies relative to their baseline by the end of the training and demonstrated significantly faster reaction times on correct trials during the n-back working memory task assessed before and after the FMθ-meditation neurofeedback protocol. No significant differences in frontal theta activity or behavior were observed in the active control participants who received age and gender matched sham neurofeedback. These findings help lay the groundwork for the development of brain training protocols and neurofeedback applications that aim to train features of the mental states and traits associated with focused-attention meditation.

Keywords: EEG; adaptive neurofeedback; attention training; frontal mid-line theta; meditation.

Copyright © 2020 Brandmeyer and Delorme.

Figures

FIGURE 1
FIGURE 1
Participants received either the neurofeedback or sham-neurofeedback training over the course of eight training from Tuesday to Friday in the first week, and from Monday to Thursday in the second week. Each Neurofeedback training session consisted of six five-min training blocks, separated by short 2–6 min breaks. The battery of tasks measuring executive functions were collected pre-neurofeedback on the first session and post-neurofeedback on the last session.
FIGURE 2
FIGURE 2
Closed loop Neurofeedback protocol starting from the acquisition of the signal to the automated ASR artifact rejection, to the FMθ extraction to the interface. When recording more than 8 channels (first and last sessions recorded 64 channels), the 8 channels Fpz, FZ, F7, F8, Cz, P7, P8, Oz were extracted from the data – although only these 8 channels were collected on all recording days, on the first day and last recording day 64 channels were recorded. Images in this figure were obtained with written consent.
FIGURE 3
FIGURE 3
For artifact rejection, each day an initial EEG baseline was measured for 1 min (start baseline), followed by six training blocks of 5 min each (block 1–6). This initial baseline was used by the ASR artifact rejection algorithm in order to optimize the filtering all feedback sessions for the day using the asr_calibrate function (default parameters of the ASR algorithm were used; variance rejection cut off of 5; block size of 10 sample to calculate covariance matrix; window size of 500 ms and window overlap of 330 ms.
FIGURE 4
FIGURE 4
(A) N-back task: visual illustration of the three levels of the n-back task. The red arrow indicates when the subject has been instructed to press the space key. (B) The Sustained Attention to response task (SART) is designed to measure a person’s ability to withhold responses to infrequent and unpredictable stimuli during a period of rapid and rhythmic responding to frequent stimuli. (C) The local-global task measures the ability to focus attention on a specific feature of a stimulus, either global or local, while resisting distraction from other features and is thought to be a relatively broad measure of conflict detection.
FIGURE 5
FIGURE 5
(A) This graph shows the enhancement across sessions, and reflects FMθ amplitude percent change for the mean of theta power for the Neurofeedback group (green) and the sham group (blue) across each training session (S1–S8) as averaged over all corresponding blocks (blocks 1–6) as compared to the first session (S1). Baseline amplitude changes are reflected by the dotted lines for each group respectively, and are show for the training relative to the first baseline measurements. Error bars indicated the standard error of the mean. (B) This graph shows the enhancement across sessions for participants identified as responders to the adaptive neurofeedback protocol (three non-responders have been removed from the analyses). Non-responders are identified as individuals whose daily scores were three or more standard deviations from the mean. As in this figure shows the enhancement across sessions, and reflects FMθ amplitude percent change for the mean of theta power for the adaptive neurofeedback group (green) and the sham group (blue) across each training session (S1–S8) as averaged over all corresponding blocks (blocks 1–6) as compared to the first session (S1). Error bars indicate the standard error of the mean.
FIGURE 6
FIGURE 6
Frequency Spectra for the Neurofeedback group (top, green) and sham group (bottom, blue) showing differences in averaged spectral power between pre (session 1) and post (session 8) for electrode Fz (feedback location). Significant differences in multiple frequency bands were observed for the Feedback group (p < 0.05, corrected for multiple comparisons; p-values are reflected as black bars above the x-axis). No significant differences were observed in the sham group.
FIGURE 7
FIGURE 7
Log reaction times are indicated on the x-axis for the n-back task. The GLM revealed a significant pre/post neurofeedback training interaction showing faster reaction times for correct n-back trials in the neurofeedback group as compared to the sham feedback group after neurofeedback training.
FIGURE 8
FIGURE 8
Results for Adaptive Neurofeedback (top) and sham (bottom) groups for gamma-power during the 2-back task, before (left) and after (middle) the adaptive neurofeedback sessions. The color bar on the right represents the statistical significance of the difference pre and post neurofeedback.

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