Reference Library

Brain–computer interfaces for communication and control

Wolpaw, J. R., et al. (2002). Clinical Neurophysiology.

Foundational framework defining BCI signal acquisition, feature translation, and control.

Über das Elektrenkephalogramm des Menschen

Berger, H. (1929). Archiv für Psychiatrie.

First recording of the human EEG, establishing non-invasive neural measurement.

Electroencephalography: Basic Principles, Clinical Applications, and Related Fields

Niedermeyer, E., & da Silva, F. L. (2004). Lippincott Williams & Wilkins.

Standard reference on EEG rhythms and clinical interpretation.

EEG alpha and theta oscillations reflect cognitive and memory performance

Klimesch, W. (1999). Brain Research Reviews.

Links alpha/theta dynamics to attention and memory.

Wearable EEG and beyond

Casson, A. J. (2019). Biomedical Engineering Letters.

Reviews dry-electrode wearable EEG feasibility and challenges.

Closed-loop brain training: the science of neurofeedback

Sitaram, R., et al. (2017). Nature Reviews Neuroscience.

Comprehensive review of neurofeedback mechanisms and evidence.

EEG-neurofeedback for optimising performance

Gruzelier, J. H. (2014). Neuroscience & Biobehavioral Reviews.

Performance applications of neurofeedback in healthy populations.

Self-regulation of frontal-midline theta via neurofeedback

Enriquez-Geppert, S., et al. (2017). Frontiers in Human Neuroscience.

Demonstrates volitional control of frontal theta.

An integrative theory of prefrontal cortex function

Miller, E. K., & Cohen, J. D. (2001). Annual Review of Neuroscience.

Defines prefrontal cortex role in executive control.

The Prefrontal Cortex

Fuster, J. M. (2015). Academic Press.

Authoritative monograph on prefrontal organization and function.

Rhythms of the Brain

Buzsáki, G. (2006). Oxford University Press.

Synthesis of neural oscillations across scales.

Neuroergonomics: The Brain at Work

Parasuraman, R., & Rizzo, M. (2007). Oxford University Press.

Foundational text on measuring cognition in real-world work.

Large-scale brain networks and psychopathology: a unifying triple network model

Menon, V. (2011). Trends in Cognitive Sciences.

Default Mode, Central Executive, and Salience network interactions.

A review of classification algorithms for EEG-based BCIs: a 10-year update

Lotte, F., et al. (2018). Journal of Neural Engineering.

Survey of EEG classification methods and best practices.

An overview of heart rate variability metrics and norms

Shaffer, F., & Ginsberg, J. P. (2017). Frontiers in Public Health.

Reference for HRV features such as RMSSD and SDNN.

Deep learning with convolutional neural networks for EEG decoding

Schirrmeister, R. T., et al. (2017). Human Brain Mapping.

Demonstrates CNN-based EEG decoding performance.

Deep learning-based electroencephalography analysis: a systematic review

Roy, Y., et al. (2019). Journal of Neural Engineering.

Maps deep learning approaches across EEG tasks.

The cost of interrupted work: more speed and stress

Mark, G., et al. (2008). CHI Conference.

Quantifies attention fragmentation and cognitive cost.

Autoreject: automated artifact rejection for MEG and EEG data

Jas, M., et al. (2017). NeuroImage.

Reproducible artifact handling for EEG pipelines.

Power failure: why small sample size undermines reliability of neuroscience

Button, K. S., et al. (2013). Nature Reviews Neuroscience.

Motivates rigorous sample sizing in validation.

The preregistration revolution

Nosek, B. A., et al. (2018). PNAS.

Pre-registration to reduce bias and false positives.

Towards new human rights in the age of neuroscience and neurotechnology

Ienca, M., & Andorno, R. (2017). Life Sciences, Society and Policy.

Proposes mental privacy and cognitive liberty rights.

Four ethical priorities for neurotechnologies and AI

Yuste, R., et al. (2017). Nature.

Privacy, agency, identity, and fairness priorities.

Neuroethical considerations: cognitive liberty and converging technologies

Sententia, W. (2004). Annals of the NY Academy of Sciences.

Defines cognitive liberty as a guiding principle.

Software as a Medical Device (SaMD): clinical evaluation guidance

U.S. FDA (2023). FDA Guidance.

Regulatory framing for software-based health claims.

General Data Protection Regulation (GDPR)

European Parliament (2016). EU Regulation 2016/679.

Data protection requirements relevant to biometric data.

Independent component analysis of electroencephalographic data

Makeig, S., et al. (1996). NeurIPS.

ICA for separating artifacts and sources in EEG.

Real-time neuroimaging and cognitive monitoring using wearable dry EEG

Mullen, T. R., et al. (2015). IEEE TBME.

Real-time wearable EEG with artifact subspace reconstruction.

Electrodermal Activity

Boucsein, W. (2012). Springer.

Definitive reference on EDA tonic/phasic measurement.

Personalized machine learning for wearable physiological signals

Dunn, J., et al. (2020). npj Digital Medicine.

Per-user calibration improves wearable model reliability.