Democratizing Music Therapy: LLM-Based Automated EEG Analysis and Progress Tracking for Low-Cost Home Devices

arxiv: 2601.12280 · v2 · submitted 2026-01-18 · 💻 cs.HC

Democratizing Music Therapy: LLM-Based Automated EEG Analysis and Progress Tracking for Low-Cost Home Devices

Huixin Xue , Guangjun Xu , Shihong Ren , Xian Gao , Ruian Tie , Zhen Zhou , Hao Liu , Yue Gao This is my paper

Pith reviewed 2026-05-16 13:41 UTC · model grok-4.3

classification 💻 cs.HC

keywords music therapyEEG analysislarge language modelshome devicesphysiological signalsautomated reportingprogress trackingcardiovascular data

0 comments p. Extension

The pith

Large language models can turn raw EEG and cardiovascular data from low-cost home devices into readable therapeutic reports and personalized music recommendations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents a prototype that applies large language models to process physiological signals for music therapy. It converts raw EEG and cardiovascular readings into plain-language reports that describe a user's state and suggest suitable music tracks. The approach targets post-session analysis to let non-experts track short-term progress without needing trained interpreters. By linking basic signal processing steps to LLM reasoning, the system aims to lower barriers for home-based, physiology-driven therapy. The work focuses on demonstrating feasibility for scalable, accessible reporting rather than real-time adaptation.

Core claim

The central claim is that a system combining signal processing modules with LLM-based reasoning agents can transform raw EEG and cardiovascular data into human-readable therapeutic reports and targeted music recommendations, thereby enabling non-expert users to understand their psychophysiological states and monitor outcomes over time in low-cost home music therapy settings.

What carries the argument

LLM reasoning agents that receive cleaned physiological numbers and generate interpretive reports plus music suggestions.

If this is right

Non-expert users gain the ability to review their psychophysiological responses after each home session.
Automated music recommendations can be generated directly from session data.
Repeated reports allow simple tracking of short-term changes in therapeutic response.
Analysis costs drop enough for wider adoption of physiology-guided music therapy at home.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar LLM pipelines could be tested on other biofeedback modalities such as heart-rate variability or skin conductance.
The same post-session reporting format might support remote therapist review by summarizing key trends.
Longer-term studies could check whether repeated use of these reports improves user adherence to therapy routines.

Load-bearing premise

An LLM given only processed EEG and cardiovascular numbers can produce accurate, clinically useful therapeutic interpretations and music recommendations without expert review or validation data.

What would settle it

A side-by-side evaluation in which licensed music therapists rate the accuracy and usefulness of LLM-generated reports on the same data sets used by the system.

read the original abstract

Home-based music therapy devices require accessible and cost-effective solutions for users to understand and track their therapeutic progress. Traditional physiological signal analysis, particularly EEG interpretation, relies heavily on domain experts, creating barriers to scalability and home adoption. Meanwhile, few experts are capable of interpreting physiological signal data while also making targeted music recommendations. While large language models (LLMs) have shown promise in various domains, their application to automated physiological report generation for music therapy represents an unexplored task. We present a prototype system that leverages LLMs to bridge this gap -- transforming raw EEG and cardiovascular data into human-readable therapeutic reports and personalized music recommendations. Unlike prior work focusing on real-time physiological adaptation during listening, our approach emphasizes post-session analysis and interpretable reporting, enabling non-expert users to comprehend their psychophysiological states and track therapeutic outcomes over time. By integrating signal processing modules with LLM-based reasoning agents, the system provides a practical and low-cost solution for short-term progress monitoring in home music therapy contexts. This work demonstrates the feasibility of applying LLMs to a novel task -- democratizing access to physiology-driven music therapy through automated, interpretable reporting.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper sketches an LLM prototype for post-session EEG reports in home music therapy but supplies no validation, results, or expert checks, so the feasibility claim stays untested.

read the letter

The main thing here is a system description for turning cleaned EEG and heart-rate numbers into readable therapy reports and music suggestions after a session. The authors position it as new because prior work focused on real-time adaptation rather than post-session summaries for non-experts. That framing holds up as a narrow but distinct angle in the HCI space around low-cost biofeedback devices. The architecture they outline—standard signal processing feeding into prompted LLM agents—looks practical on paper and could be straightforward to implement with existing tools. They also keep the scope realistic by limiting it to short-term home tracking instead of claiming broad clinical impact. That said, the manuscript gives no numbers at all: no accuracy checks against clinicians, no user-study scores on whether the reports feel useful or safe, and no error analysis on the LLM outputs. The central feasibility argument therefore rests entirely on the assumption that an off-the-shelf LLM will produce reliable interpretations from physiological features without further tuning or validation data. That assumption is common in early prototypes but needs testing before the work can move forward. Minor points include the usual citation gaps around existing EEG-to-text efforts in other domains, but nothing load-bearing. This paper is mainly for HCI or applied biofeedback groups who want a starting template for similar apps; serious neuroscience or music-cognition readers will find little to engage with. It does not yet show the kind of evidence that would justify sending it to referees, because the core claim remains a description rather than a demonstrated result. I would not bring it to a reading group or cite it until they add at least basic expert ratings or a small user trial.

Referee Report

3 major / 2 minor

Summary. The paper presents a prototype system architecture that combines signal processing modules with LLM-based reasoning agents to convert raw EEG and cardiovascular signals from low-cost home devices into human-readable therapeutic reports and personalized music recommendations. The work focuses on post-session analysis for progress tracking in home music therapy, positioning LLMs as a bridge to reduce reliance on scarce domain experts who can both interpret physiological data and suggest music interventions. The central claim is that this approach demonstrates the feasibility of automated, interpretable reporting for democratizing physiology-driven music therapy.

Significance. If the feasibility claim were substantiated through validation, the work could meaningfully expand access to music therapy by enabling non-experts to monitor psychophysiological states at home. The emphasis on post-session interpretability rather than real-time adaptation distinguishes it from prior physiological music systems. However, the manuscript supplies only an untested architectural description, so its significance remains prospective rather than demonstrated.

major comments (3)

[Abstract and §4] Abstract and §4 (System Architecture): The manuscript asserts that the prototype 'demonstrates the feasibility' of LLM-based therapeutic reporting, yet provides no quantitative evaluation whatsoever—no accuracy metrics for report generation, no expert-clinician comparison ratings, no user-study measures of usefulness or safety, and no error analysis of music recommendations. This absence directly undermines the central feasibility claim, as the outputs are described but never tested against ground truth or clinical standards.
[§5] §5 (Evaluation) or equivalent results section: No validation data, baselines, or ablation studies are reported. The feasibility argument therefore rests entirely on the untested premise that prompting an LLM on cleaned EEG/cardiovascular numbers will yield clinically reliable interpretations and recommendations without expert oversight.
[§6] §6 (Discussion/Limitations): The manuscript lacks any analysis of risks such as hallucinated therapeutic advice, inter-user variability in EEG interpretation, or liability implications of automated recommendations, all of which are load-bearing for a system intended for unsupervised home use.

minor comments (2)

[§4] The description of prompt engineering and signal-cleaning steps could be expanded with concrete examples or pseudocode to allow replication.
[Figures] Figure captions and architecture diagrams would benefit from explicit data-flow arrows and component labels to improve clarity for readers unfamiliar with LLM agent pipelines.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful comments, which help clarify the scope and limitations of our prototype system. We agree that the manuscript presents an untested architectural design and that stronger claims require empirical validation. In our revision, we will adjust the language to accurately reflect the current contribution as a proof-of-concept and expand the discussion of risks and future validation needs.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (System Architecture): The manuscript asserts that the prototype 'demonstrates the feasibility' of LLM-based therapeutic reporting, yet provides no quantitative evaluation whatsoever—no accuracy metrics for report generation, no expert-clinician comparison ratings, no user-study measures of usefulness or safety, and no error analysis of music recommendations. This absence directly undermines the central feasibility claim, as the outputs are described but never tested against ground truth or clinical standards.

Authors: We accept this criticism. The term 'demonstrates the feasibility' in the abstract and system architecture section was intended to refer to the successful integration of signal processing with LLM agents in a working prototype, not to clinical or quantitative validation. To address this, we will revise the abstract and §4 to replace 'demonstrates the feasibility' with 'presents a prototype architecture for' and explicitly note that this is a design proposal without empirical testing. This change will be made in the next version of the manuscript. revision: yes
Referee: [§5] §5 (Evaluation) or equivalent results section: No validation data, baselines, or ablation studies are reported. The feasibility argument therefore rests entirely on the untested premise that prompting an LLM on cleaned EEG/cardiovascular numbers will yield clinically reliable interpretations and recommendations without expert oversight.

Authors: We agree that the manuscript lacks an evaluation section with data or studies, as the work focuses on system design rather than validation. The feasibility argument is based on the architectural feasibility, not clinical reliability. We will revise by adding or expanding §5 to include a clear statement that no validation has been conducted and to outline planned future work, such as expert clinician evaluations and user studies for safety and usefulness. revision: yes
Referee: [§6] §6 (Discussion/Limitations): The manuscript lacks any analysis of risks such as hallucinated therapeutic advice, inter-user variability in EEG interpretation, or liability implications of automated recommendations, all of which are load-bearing for a system intended for unsupervised home use.

Authors: This is a valid point. The current limitations section primarily addresses technical aspects but does not sufficiently cover clinical and ethical risks. We will revise §6 to include a new subsection discussing risks including potential LLM hallucinations in generating therapeutic advice, variability in EEG signals and interpretations across users, and liability concerns for automated recommendations in home environments. We will also propose safeguards such as user disclaimers and recommendations for professional oversight. revision: yes

Circularity Check

0 steps flagged

No circularity: system prototype demonstration without derivations or fitted predictions

full rationale

The paper presents a prototype architecture that combines signal processing modules with prompted LLMs to generate therapeutic reports and music recommendations from EEG and cardiovascular data. No equations, parameter fitting, uniqueness theorems, or self-citation chains appear in the provided text. The central claim is a feasibility demonstration of the integrated system rather than any predictive or first-principles derivation that could reduce to its own inputs by construction. This matches the default expectation of no significant circularity for non-mathematical system papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the untested premise that current LLMs can reliably translate physiological signals into therapeutic language; no free parameters, new entities, or formal axioms are introduced beyond standard signal-processing and LLM-prompting assumptions.

axioms (1)

domain assumption LLMs prompted on processed EEG and cardiovascular features can generate clinically meaningful therapeutic reports and music recommendations
Invoked in the system description as the bridge between signal processing and human-readable output

pith-pipeline@v0.9.0 · 5518 in / 1197 out tokens · 39897 ms · 2026-05-16T13:41:57.292850+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · 3 internal anchors

[1]

Democratizing Music Therapy: LLM-Based Automated EEG Analysis and Progress Tracking for Low-Cost Home Devices

INTRODUCTION Music therapy has demonstrated efficacy in addressing stress, anxiety, and sleep disorders, driving growing interest in home-based therapeutic solutions. As consumer-grade †Equal Contribution ∗Corresponding Authors Copyright: ©2026 Huixin Xue et al. This is an open-access article dis- tributed under the terms of the Creative Commons Attributi...

work page internal anchor Pith review Pith/arXiv arXiv 2026
[2]

2.1 Experiment and Data Processing Prior to participation, all individuals provided written in- formed consent following institutional ethics approval

FRAMEWORK AND PIPELINE Figure 1: Pipeline of the proposed LLM-based framework. 2.1 Experiment and Data Processing Prior to participation, all individuals provided written in- formed consent following institutional ethics approval. The form explains the purpose of the study, which is to explore how brain and heart signals can be used to provide person- ali...

work page
[3]

MODEL 3.1 System Architecture We propose a Large Language Model (LLM)-based intelli- gent agent system for automated EEG signal analysis and personalized music therapy recommendation. The system architecture comprises three primary components: a) an EEG signal processing module for feature extrac- tion, b) an LLM-powered cognitive reasoning agent, and c) ...

work page
[4]

Frequency Resonance: Employ Theta-wave pulsed sounds at 5.33 Hz (e.g., low-frequency singing bowl vibrations) combined with Alpha-wave ambient sounds around 12 Hz (e.g., rainfall soundscapes) to reinforce relax- ation anchoring

work page
[5]

Dynamic Regulation: Intermittently present violin harmonic segments corresponding to the 21.51 Hz Beta-wave range using an acous- tic braking strategy to modulate excessive cortical excitability

work page
[6]

name": "process_eeg

Integrated Protocol: Utilize binaural beats with a 14 Hz signal delivered to the left ear (focus enhancement) and an 8 Hz signal de- livered to the right ear (relaxation promotion), facilitating functional rebalancing via neuro- plasticity. Note: Prior to daily use, a 2-minute-and-4-second 5.33 Hz sinusoidal breathing-synchronization train- ing is recomme...

work page
[7]

relaxed,

EXPERIMENTS AND V ALIDATION 4.1 Hardware Setup Our system prioritizes accessibility and home deployability over clinical-grade precision. Unlike medical EEG sys- tems requiring trained technicians and costing thousands of dollars, we employ consumer-grade devices that everyday users can operate independently. The primary physiological signal acquisition d...

work page
[8]

DISCUSSION 5.1 Key Findings in the Expert Report Evaluation The expert report evaluation mainly focuses on the cor- rectness of spectral decomposition and feature extraction, alignment of interpretations with neurophysiological princi- ples, and relevance of music recommendations to identified brain states. The closed-source LLMs, i.e., Qwen-plus and GPT-...

work page
[9]

CONCLUSIONS We proposed an end-to-end multimodal post-hoc music recommendation model and demonstrated its feasibility within an AI-based music therapy pod. In contrast to prior work emphasizing real-time physiological feedback, this study focuses on post-experience analysis and stage-based music recommendation, which is particularly suitable for structure...

work page 2023
[10]

A Scoping Review of Emerging AI Technologies in Mental Health Care: To- wards Personalized Music Therapy,

N. Santos and G. Bernardes, “A Scoping Review of Emerging AI Technologies in Mental Health Care: To- wards Personalized Music Therapy,” inConference on Sonification of Health and Environmental Data, Stock- holm, Sweden, Jan. 2025

work page 2025
[11]

Music for a Solo Performer, live perfor- mance,

A. Lucier, “Music for a Solo Performer, live perfor- mance,” 1965

work page 1965
[12]

The Performing Brain,

D. Rosenboom, “The Performing Brain,”Computer Music Journal, vol. 14, no. 1, p. 48, 1990. [On- line]. Available: https://www.jstor.org/stable/3680116? origin=crossref

work page arXiv 1990
[13]

Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory,

H.-V . V . Ngo, T. Martinetz, J. Born, and M. M ¨olle, “Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory,”Neuron, vol. 78, no. 3, pp. 545–553, May 2013. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/ S0896627313002304

work page 2013
[14]

Phase-locked auditory stimulation of theta oscillations during rapid eye movement sleep,

M. O. Harrington, J. E. Ashton, H.-V . V . Ngo, and S. A. Cairney, “Phase-locked auditory stimulation of theta oscillations during rapid eye movement sleep,” Sleep, vol. 44, no. 4, p. zsaa227, Apr. 2021. [Online]. Available: https://academic.oup.com/sleep/article/doi/ 10.1093/sleep/zsaa227/5960115

work page doi:10.1093/sleep/zsaa227/5960115 2021
[15]

A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans,

H. Hebron, B. Lugli, R. Dimitrova, V . Jaramillo, L. R. Yeh, E. Rhodes, N. Grossman, D.-J. Dijk, and I. R. Violante, “A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans,”PLOS Biology, vol. 22, no. 6, p. e3002651, Jun. 2024. [Online]. Available: https://dx.plos.org/10.1371/journal.pbio.3002651

work page doi:10.1371/journal.pbio.3002651 2024
[16]

Reflex-in: Generate Music on the Web with Real-time Brain Wave,

Anonymous, M. Buffa, L. Pottier, Y . Yu, and G. Schalk, “Reflex-in: Generate Music on the Web with Real-time Brain Wave,” inCompanion Proceedings of the ACM Web Conference 2023. Austin TX USA: ACM, Apr. 2023, pp. 598–600. [Online]. Available: https://dl.acm.org/doi/10.1145/3543873.3587315

work page doi:10.1145/3543873.3587315 2023
[17]

Binaural Beat Technology in Humans: A Pilot Study To Assess Psychologic and Physiologic Effects,

H. Wahbeh, C. Calabrese, and H. Zwickey, “Binaural Beat Technology in Humans: A Pilot Study To Assess Psychologic and Physiologic Effects,”The Journal of Alternative and Complementary Medicine, vol. 13, no. 1, pp. 25–32, Jan. 2007. [Online]. Available: http: //www.liebertpub.com/doi/10.1089/acm.2006.6196

work page doi:10.1089/acm.2006.6196 2007
[18]

On the Frequency Limits of Binaural Beats,

J. C. R. Licklider, J. C. Webster, and J. M. Hedlun, “On the Frequency Limits of Binaural Beats,” The Journal of the Acoustical Society of America, vol. 22, no. 4, pp. 468–473, Jul. 1950. [Online]. Available: https://pubs.aip.org/jasa/article/22/4/468/ 619473/On-the-Frequency-Limits-of-Binaural-Beats

work page 1950
[19]

Control of the Heart Rate by External Stimuli,

P. T. Bason and B. G. Celler, “Control of the Heart Rate by External Stimuli,”Nature, vol. 238, no. 5362, pp. 279–280, Aug. 1972. [Online]. Available: https://www.nature.com/articles/238279a0

work page 1972
[20]

Adopting a music-to-heart rate alignment strategy to measure the impact of music and its tempo on human heart rate,

E. Van Dyck, J. Six, E. Soyer, M. Denys, I. Bardijn, and M. Leman, “Adopting a music-to-heart rate alignment strategy to measure the impact of music and its tempo on human heart rate,”Musicae Scientiae, vol. 21, no. 4, pp. 390–404, Dec. 2017. [Online]. Available: https://journals.sagepub.com/doi/10.1177/ 1029864917700706

work page 2017
[21]

Integrating Physiological Data with Large Language Models for Empathic Human- AI Interaction,

P. Dongre, M. Behravan, K. Gupta, M. Billinghurst, and D. Gra ˇcanin, “Integrating Physiological Data with Large Language Models for Empathic Human- AI Interaction,” 2024. [Online]. Available: https: //arxiv.org/abs/2404.15351

work page arXiv 2024
[22]

PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing,

Y . Xie, B. Zhao, M. Dai, J.-P. Zhou, Y . Sun, T. Tan, W. Xie, L. Shen, and Z. Yu, “PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing,” 2025. [Online]. Available: https://arxiv.org/abs/2505.03621

work page arXiv 2025
[23]

Retrieval- augmented generation for knowledge-intensive NLP tasks,

P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. K ¨uttler, M. Lewis, W.-t. Yih, T. Rockt¨aschel, S. Riedel, and D. Kiela, “Retrieval- augmented generation for knowledge-intensive NLP tasks,” inProceedings of the 34th International Con- ference on Neural Information Processing Systems, ser. NIPS ’20. Red Hook, NY , USA: Curran Associ...

work page 2020
[24]

Retrieval-Augmented Generation for Large Language Models: A Survey

Y . Gao, Y . Xiong, X. Gao, K. Jia, J. Pan, Y . Bi, Y . Dai, J. Sun, M. Wang, and H. Wang, “Retrieval-Augmented Generation for Large Language Models: A Survey,” 2024. [Online]. Available: https://arxiv.org/abs/2312.10997

work page internal anchor Pith review Pith/arXiv arXiv 2024
[25]

Qwen3 Technical Report

A. Yang, A. Li, B. Yang, B. Zhang, B. Hui, and et al, “Qwen3 Technical Report,” 2025. [Online]. Available: https://arxiv.org/abs/2505.09388

work page internal anchor Pith review Pith/arXiv arXiv 2025

[1] [1]

Democratizing Music Therapy: LLM-Based Automated EEG Analysis and Progress Tracking for Low-Cost Home Devices

INTRODUCTION Music therapy has demonstrated efficacy in addressing stress, anxiety, and sleep disorders, driving growing interest in home-based therapeutic solutions. As consumer-grade †Equal Contribution ∗Corresponding Authors Copyright: ©2026 Huixin Xue et al. This is an open-access article dis- tributed under the terms of the Creative Commons Attributi...

work page internal anchor Pith review Pith/arXiv arXiv 2026

[2] [2]

2.1 Experiment and Data Processing Prior to participation, all individuals provided written in- formed consent following institutional ethics approval

FRAMEWORK AND PIPELINE Figure 1: Pipeline of the proposed LLM-based framework. 2.1 Experiment and Data Processing Prior to participation, all individuals provided written in- formed consent following institutional ethics approval. The form explains the purpose of the study, which is to explore how brain and heart signals can be used to provide person- ali...

work page

[3] [3]

MODEL 3.1 System Architecture We propose a Large Language Model (LLM)-based intelli- gent agent system for automated EEG signal analysis and personalized music therapy recommendation. The system architecture comprises three primary components: a) an EEG signal processing module for feature extrac- tion, b) an LLM-powered cognitive reasoning agent, and c) ...

work page

[4] [4]

Frequency Resonance: Employ Theta-wave pulsed sounds at 5.33 Hz (e.g., low-frequency singing bowl vibrations) combined with Alpha-wave ambient sounds around 12 Hz (e.g., rainfall soundscapes) to reinforce relax- ation anchoring

work page

[5] [5]

Dynamic Regulation: Intermittently present violin harmonic segments corresponding to the 21.51 Hz Beta-wave range using an acous- tic braking strategy to modulate excessive cortical excitability

work page

[6] [6]

name": "process_eeg

Integrated Protocol: Utilize binaural beats with a 14 Hz signal delivered to the left ear (focus enhancement) and an 8 Hz signal de- livered to the right ear (relaxation promotion), facilitating functional rebalancing via neuro- plasticity. Note: Prior to daily use, a 2-minute-and-4-second 5.33 Hz sinusoidal breathing-synchronization train- ing is recomme...

work page

[7] [7]

relaxed,

EXPERIMENTS AND V ALIDATION 4.1 Hardware Setup Our system prioritizes accessibility and home deployability over clinical-grade precision. Unlike medical EEG sys- tems requiring trained technicians and costing thousands of dollars, we employ consumer-grade devices that everyday users can operate independently. The primary physiological signal acquisition d...

work page

[8] [8]

DISCUSSION 5.1 Key Findings in the Expert Report Evaluation The expert report evaluation mainly focuses on the cor- rectness of spectral decomposition and feature extraction, alignment of interpretations with neurophysiological princi- ples, and relevance of music recommendations to identified brain states. The closed-source LLMs, i.e., Qwen-plus and GPT-...

work page

[9] [9]

CONCLUSIONS We proposed an end-to-end multimodal post-hoc music recommendation model and demonstrated its feasibility within an AI-based music therapy pod. In contrast to prior work emphasizing real-time physiological feedback, this study focuses on post-experience analysis and stage-based music recommendation, which is particularly suitable for structure...

work page 2023

[10] [10]

A Scoping Review of Emerging AI Technologies in Mental Health Care: To- wards Personalized Music Therapy,

N. Santos and G. Bernardes, “A Scoping Review of Emerging AI Technologies in Mental Health Care: To- wards Personalized Music Therapy,” inConference on Sonification of Health and Environmental Data, Stock- holm, Sweden, Jan. 2025

work page 2025

[11] [11]

Music for a Solo Performer, live perfor- mance,

A. Lucier, “Music for a Solo Performer, live perfor- mance,” 1965

work page 1965

[12] [12]

The Performing Brain,

D. Rosenboom, “The Performing Brain,”Computer Music Journal, vol. 14, no. 1, p. 48, 1990. [On- line]. Available: https://www.jstor.org/stable/3680116? origin=crossref

work page arXiv 1990

[13] [13]

Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory,

H.-V . V . Ngo, T. Martinetz, J. Born, and M. M ¨olle, “Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory,”Neuron, vol. 78, no. 3, pp. 545–553, May 2013. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/ S0896627313002304

work page 2013

[14] [14]

Phase-locked auditory stimulation of theta oscillations during rapid eye movement sleep,

M. O. Harrington, J. E. Ashton, H.-V . V . Ngo, and S. A. Cairney, “Phase-locked auditory stimulation of theta oscillations during rapid eye movement sleep,” Sleep, vol. 44, no. 4, p. zsaa227, Apr. 2021. [Online]. Available: https://academic.oup.com/sleep/article/doi/ 10.1093/sleep/zsaa227/5960115

work page doi:10.1093/sleep/zsaa227/5960115 2021

[15] [15]

A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans,

H. Hebron, B. Lugli, R. Dimitrova, V . Jaramillo, L. R. Yeh, E. Rhodes, N. Grossman, D.-J. Dijk, and I. R. Violante, “A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans,”PLOS Biology, vol. 22, no. 6, p. e3002651, Jun. 2024. [Online]. Available: https://dx.plos.org/10.1371/journal.pbio.3002651

work page doi:10.1371/journal.pbio.3002651 2024

[16] [16]

Reflex-in: Generate Music on the Web with Real-time Brain Wave,

Anonymous, M. Buffa, L. Pottier, Y . Yu, and G. Schalk, “Reflex-in: Generate Music on the Web with Real-time Brain Wave,” inCompanion Proceedings of the ACM Web Conference 2023. Austin TX USA: ACM, Apr. 2023, pp. 598–600. [Online]. Available: https://dl.acm.org/doi/10.1145/3543873.3587315

work page doi:10.1145/3543873.3587315 2023

[17] [17]

Binaural Beat Technology in Humans: A Pilot Study To Assess Psychologic and Physiologic Effects,

H. Wahbeh, C. Calabrese, and H. Zwickey, “Binaural Beat Technology in Humans: A Pilot Study To Assess Psychologic and Physiologic Effects,”The Journal of Alternative and Complementary Medicine, vol. 13, no. 1, pp. 25–32, Jan. 2007. [Online]. Available: http: //www.liebertpub.com/doi/10.1089/acm.2006.6196

work page doi:10.1089/acm.2006.6196 2007

[18] [18]

On the Frequency Limits of Binaural Beats,

J. C. R. Licklider, J. C. Webster, and J. M. Hedlun, “On the Frequency Limits of Binaural Beats,” The Journal of the Acoustical Society of America, vol. 22, no. 4, pp. 468–473, Jul. 1950. [Online]. Available: https://pubs.aip.org/jasa/article/22/4/468/ 619473/On-the-Frequency-Limits-of-Binaural-Beats

work page 1950

[19] [19]

Control of the Heart Rate by External Stimuli,

P. T. Bason and B. G. Celler, “Control of the Heart Rate by External Stimuli,”Nature, vol. 238, no. 5362, pp. 279–280, Aug. 1972. [Online]. Available: https://www.nature.com/articles/238279a0

work page 1972

[20] [20]

Adopting a music-to-heart rate alignment strategy to measure the impact of music and its tempo on human heart rate,

E. Van Dyck, J. Six, E. Soyer, M. Denys, I. Bardijn, and M. Leman, “Adopting a music-to-heart rate alignment strategy to measure the impact of music and its tempo on human heart rate,”Musicae Scientiae, vol. 21, no. 4, pp. 390–404, Dec. 2017. [Online]. Available: https://journals.sagepub.com/doi/10.1177/ 1029864917700706

work page 2017

[21] [21]

Integrating Physiological Data with Large Language Models for Empathic Human- AI Interaction,

P. Dongre, M. Behravan, K. Gupta, M. Billinghurst, and D. Gra ˇcanin, “Integrating Physiological Data with Large Language Models for Empathic Human- AI Interaction,” 2024. [Online]. Available: https: //arxiv.org/abs/2404.15351

work page arXiv 2024

[22] [22]

PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing,

Y . Xie, B. Zhao, M. Dai, J.-P. Zhou, Y . Sun, T. Tan, W. Xie, L. Shen, and Z. Yu, “PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing,” 2025. [Online]. Available: https://arxiv.org/abs/2505.03621

work page arXiv 2025

[23] [23]

Retrieval- augmented generation for knowledge-intensive NLP tasks,

P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. K ¨uttler, M. Lewis, W.-t. Yih, T. Rockt¨aschel, S. Riedel, and D. Kiela, “Retrieval- augmented generation for knowledge-intensive NLP tasks,” inProceedings of the 34th International Con- ference on Neural Information Processing Systems, ser. NIPS ’20. Red Hook, NY , USA: Curran Associ...

work page 2020

[24] [24]

Retrieval-Augmented Generation for Large Language Models: A Survey

Y . Gao, Y . Xiong, X. Gao, K. Jia, J. Pan, Y . Bi, Y . Dai, J. Sun, M. Wang, and H. Wang, “Retrieval-Augmented Generation for Large Language Models: A Survey,” 2024. [Online]. Available: https://arxiv.org/abs/2312.10997

work page internal anchor Pith review Pith/arXiv arXiv 2024

[25] [25]

Qwen3 Technical Report

A. Yang, A. Li, B. Yang, B. Zhang, B. Hui, and et al, “Qwen3 Technical Report,” 2025. [Online]. Available: https://arxiv.org/abs/2505.09388

work page internal anchor Pith review Pith/arXiv arXiv 2025