A Minimalist Brain-Computer Musical Interface for Real-Time Emotion-Driven Sonification: System Design and Preliminary Evaluation

• URL: http://arxiv.org/abs/2606.01473v1
• Date: Sun, 31 May 2026 22:22:15 GMT
• Title: A Minimalist Brain-Computer Musical Interface for Real-Time Emotion-Driven Sonification: System Design and Preliminary Evaluation
• Abstract summary: This paper presents a minimalist brain-computer Musical Interface (BCMI) that functions as a real-time affective sonification system.<n>The system integrates wireless EEG acquisition, real-time Python signal processing, and Live-based music generation synchronized via Lab Streaming Layer.<n> Linear mixed-effects analyses found no significant effects of target emotion or time, indicating that the frontal alpha asymmetry signal did not reliably distinguish instructed emotional states.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a minimalist brain-computer Musical Interface (BCMI) that functions as a real-time affective sonification system, translating prefrontal EEG activity into adaptive music. Emotional valence is estimated from frontal alpha asymmetry (AF7/AF8) and mapped to musical features such as mode, tempo, rhythmic density, and pitch register through a stochastic generative algorithm. The system integrates wireless EEG acquisition, real-time Python signal processing, and Ableton Live-based music generation synchronized via Lab Streaming Layer. An experiment with 22 participants investigated whether intentional emotional self-induction could modulate the BCMI neurofeedback signal. Linear mixed-effects analyses found no significant effects of target emotion or time, indicating that the frontal alpha asymmetry signal did not reliably distinguish instructed emotional states. Individual differences, including musical training and acting experience, explained more variance than the experimental manipulation, which accounted for only 0.40\% of total signal variance. These findings highlight the challenges of using frontal alpha asymmetry as a voluntary control signal for closed-loop emotion regulation and suggest methodological directions for future BCMI research.

Related papers

• Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery [64.82869118243723]
  We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement.<n>Motivated by the promise of BCI-driven robotics, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner.
  arXiv  Detail & Related papers  (2026-03-03T17:41:42Z)
• Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition [56.00118641432005]
  We propose a Memory-guided Prototypical Co-occurrence Learning framework that explicitly models emotion co-occurrence patterns.<n>Inspired by human cognitive memory systems, we introduce a memory retrieval strategy to extract semantic-level co-occurrence associations.<n>Our model learns affectively informative representations for accurate emotion distribution prediction.
  arXiv  Detail & Related papers  (2026-02-24T04:11:25Z)
• E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis [54.763420895859035]
  We present ELLM2-EEG-to-Emotion Large Language Model, first MLLM framework for interpretable emotion analysis from EEG.<n>ELLM integrates a pretrained EEG encoder with Q-based LLMs through learnable projection layers, employing a multi-stage training pipeline.<n>Experiments on the dataset across seven emotion categories demonstrate that ELLM2-EEG-to-Emotion Large Language Model achieves excellent performance on emotion classification.
  arXiv  Detail & Related papers  (2026-01-11T13:21:20Z)
• BREATH: A Bio-Radar Embodied Agent for Tonal and Human-Aware Diffusion Music Generation [3.2646887494398205]
  We present a multimodal system for personalized music generation that integrates physiological sensing, LLM-based reasoning, and controllable audio synthesis.<n>A millimeter-wave radar sensor non-invasively captures heart rate and respiration rate.<n>These physiological signals are interpreted by a reasoning agent to infer symbolic musical descriptors, such as tempo, mood intensity, and traditional Chinese pentatonic modes.
  arXiv  Detail & Related papers  (2025-09-09T12:26:20Z)
• EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation [12.707059419820848]
  This paper presents the EEG Emotion Copilot, which first recognizes emotional states directly from EEG signals.<n>It then generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records.<n>The proposed copilot is expected to advance the application of affective computing in the medical domain.
  arXiv  Detail & Related papers  (2024-09-30T19:15:05Z)
• Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation [34.726185927120355]
  We employ music signals as a supervisory modality to EEG, aiming to project their semantic correspondence onto a common representation space. We utilize a bi-modal framework by combining an LSTM-based attention model to process EEG and a pre-trained model for music tagging, along with a reverse domain discriminator to align the distributions of the two modalities. The resulting framework can be utilized for emotion recognition both directly, by performing supervised predictions from either modality, and indirectly, by providing relevant music samples to EEG input queries.
  arXiv  Detail & Related papers  (2022-02-20T07:32:12Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)
• TSception: A Deep Learning Framework for Emotion Detection Using EEG [11.444502210936776]
  We propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG) TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously. TSception achieves a high classification accuracy of 86.03%, which outperforms the prior methods significantly.
  arXiv  Detail & Related papers  (2020-04-02T02:10:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.