Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG

Abstract

Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain‐imaging problem with immediate relevance to developing brain–computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task‐related cortical engagement was inferred from beta band (17–25 Hz) power decrements estimated using a frequency‐resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta‐power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor‐imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor‐versus‐nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands‐versus‐word and hands‐versus‐sub, respectively. A multivariate Gaussian‐process classifier provided an accuracy rate of 60% for the four‐way (HANDS‐FEET‐WORD‐SUB) classification problem. Individual task performance was revealed by within‐subject correlations of beta‐decrements. Beta‐power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke.

The controllosphere: The neural origin of cognitive effort.

“…If successful, then LCT can be applied to test specific predictions about cognitive effort in humans. For example, the monkey brain–computer interface experiment conducted by Sadtler et al (2014; see “The Intrinsic Manifold”) could be replicated in humans using magnetoencephalography (e.g., Corsi et al, 2021; Stiso et al, 2020; Youssofzadeh et al, 2023). RNNs reproducing the intrinsic manifold could then either be constructed by hand (e.g., Wärnberg & Kumar, 2019) or reverse-engineered directly from the neural data (Durstewitz et al, 2023; see Box 3).…”

Event-Related EEG Desynchronization Reveals Enhanced Motor Imagery From the Third Person Perspective by Manipulating Sense of Body Ownership With Virtual Reality for Stroke Patients

“…4) in the E-MI paradigm compared to the C-MI paradigm. These channels were related to motor-related regions (i.e., supplementary motor area [52] and somatosensory association cortex [76]). Note that the topographical ERSP maps demonstrated stronger ERDs at the CP3/CP4 electrodes compared to the C3/C4 electrodes.…”

“…We used EEGLAB [49] (version 2021.1) to analyze the EEG data. The EEG signals were first filtered using a band-pass filter with a low cutoff frequency of 0.1 Hz [50] and a high cutoff frequency of 40 Hz to remove the power interference and other noises while retaining the signals in the α and β bands [51], [52]. Subsequently, 6 s epochs were extracted for each trial, ranging from -2 to +4 s around the MI task onset, similar to that in previous studies [24], [53], [54].…”