Key Highlights

• 20+ real-time feature modalities — sensor power, ERD/ERS, connectivity, cross-frequency coupling, graph-theory metrics, and more
• Sensor and source-space processing using MNE inverse operators (eLORETA, dSPM, MNE)
• Live artifact correction — ORICA, adaptive LMS, ASR, and real-time Maxwell filtering for MEG
• Adaptive feedback protocols — threshold, z-score, reinforcement learning, sham, and operant schedules
• Nine real-time visualisation windows including scalp topomaps, 3-D brain surface, and TFR heatmaps
• Dual feedback output via OSC (Max/MSP, SuperCollider) and LSL (PsychoPy, OpenViBE, BCI2000)

Install

pip install mne-rt

The library was developed with support from the Swiss National Science Foundation (grant 208164).

Abstract

Tinnitus, the perception of sound without an external source, affects many individuals, yet its impact on the brain’s functional connectome remains underexplored. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index, and coherence, rely on pairwise comparisons between activity of macro-scale brain regions, limiting holistic characterization. We used an approach that estimates the entire connectivity structure by analyzing all time-courses simultaneously, robust even for short recordings and suitable for real-time applications. Using resting-state MEG from tinnitus patients and controls, learned connectomes outperformed correlation-based ones in fingerprinting individuals across test/retest. Group analyses revealed altered FC across multiple frequency bands, impacting default mode, auditory, visual, and salience networks, indicating large-scale reorganization. Tinnitus exhibited highly individualized whole-brain FC profiles, highlighting the importance of individual variability and paving the way for personalized models to optimize patient-specific interventions.

Links

Published paper

Abstract

Tinnitus is defined as the perception of sound without an external source. Its perceptual suppression or on/off states remain poorly understood. This study investigates neural traits linked to brief acoustic tinnitus suppression (BATS) using naive resting-state EEG (closed eyes) from 102 individuals. A set of EEG features (band power, entropy, aperiodic slope and offset of the EEG spectrum, and connectivity) and standard classifiers were applied achieving consistent high accuracy across data splits: 98% for sensor and 86% for source models. The Random Forest model outperformed other classifiers by excelling in robustness and reduction of overfitting. It identified several key EEG features, most prominently alpha and gamma frequency band power. Gamma power was stronger in the left auditory network, while alpha power dominated the right hemisphere. Aperiodic features were normalized in individuals with BATS. Additionally, hyperconnected auditory-limbic networks in BATS suggest sensory gating may aid suppression. These findings demonstrate robust classification of BATS status, revealing distinct neural traits between tinnitus subpopulations. Our work emphasizes the role of neural mechanisms in predicting and managing tinnitus suppression. Moreover, it advances the understanding of effective feature selection, model choice, and validation strategies for analyzing clinical neurophysiological data in general.

Links

Published paper

Abstract

In a complete theory there is an element corresponding to each element of reality. A sufficient condition for the reality of a physical quantity is the possibility of predicting it with certainty, without disturbing the system. In quantum mechanics in the case of two physical quantities described by non-commuting operators, the knowledge of one precludes the knowledge of the other. Then either (1) the description of reality given by the wave function in quantum mechanics is not complete or (2) these two quantities cannot have simultaneous reality. Consideration of the problem of making predictions concerning a system on the basis of measurements made on another system that had previously interacted with it leads to the result that if (1) is false then (2) is also false. One is thus led to conclude that the description of reality as given by a wave function is not complete.

Links

Published paper