One hundred years of EEG for brain and behaviour research Faisal Mushtaq, Dominik Welke, Anne Gallagher, Yuri G. Pavlov, Layla Kouara, Jorge Bosch-Bayard, Jasper J. F. van den Bosch, Mahnaz Arvaneh, Amy R. Bland, Maximilien Chaumon, Cornelius Borck, Xun He, Steven J. Luck, Maro G. Machizawa, Cyril Pernet, Aina Puce, Sidney Segalowitz, Christine Rogers, Muhammad Awais, Claudio Babiloni, Neil W. Bailey, Sylvain Baillet, Robert C. A. Bendall, Daniel Brady, Maria L. Bringas-Vega, Niko Busch, Ana Calzada-Reyes, Armand Chatard, Peter E. Clayson, Michael X. Cohen, Jonathan Cole, Martin Constant, Alexandra Corneyllie, Damien Coyle, Damian Cruse, Ioannis Delis, Arnaud Delorme, Damien Fair, Tiago H. Falk, Matthias Gamer, Giorgio Ganis, Kilian Gloy, Samantha Gregory, Cameron Hassall, Katherine Hiley, Richard B. Ivry, Karim Jerbi, Michael Jenkins, Jakob Kaiser, Andreas Keil, Robert T. Knight, Silvia Kochen, Boris Kotchoubey, Olave Krigolson, Nicolas Langer, Heinrich R. Liesefeld, Sarah Lippé, Raquel E. London, Annmarie MacNamara, Scott Makeig, Welber Marinovic, Eduardo Martínez-Montes, Aleya A. Marzuki, Ryan K. Mathew, Christoph Michel, José d. R. Millán, Mark Mon-Williams, Lilia Morales-Chacón, Richard Naar, Gustav Nilsonne, Guiomar Niso, Erika Nyhus, Robert Oostenveld, Katharina Paul, Walter Paulus, Daniela M. Pfabigan, Gilles Pourtois, Stefan Rampp, Manuel Rausch, Kay Robbins, Paolo M. Rossini, Manuela Ruzzoli, Barbara Schmidt, Magdalena Senderecka, Narayanan Srinivasan, Yannik Stegmann, Paul M. Thompson, Mitchell Valdes-Sosa, Melle J. W. van der Molen, Domenica Veniero, Edelyn Verona, Bradley Voytek, Dezhong Yao, Alan C. Evans, Pedro Valdes-Sosa The final publication is available at Springer via https://doi.org/10.1038/s41562-024-01941-5 Permanent link to this version https://hdl.handle.net/20.500.14078/3916 License All Rights Reserved This document has been made available through RO@M (Research Online at Macewan), a service of MacEwan University Library. Please contact roam@macewan.ca for additional information. https://doi.org/10.1038/s41562-024-01941-5 https://hdl.handle.net/20.500.14078/3916 https://roam.macewan.ca/ mailto:roam@macewan.ca 100 years of EEG for brain and behaviour research Faisal Mushtaq1,2†, Dominik Welke1, Anne Gallagher3,4, Yuri G. Pavlov5, Layla Kouara1, Jorge Bosch-Bayard6, Jasper J. F. van den Bosch1, Mahnaz Arvaneh7, Amy R. Bland8, Maximilien Chaumon9, Cornelius Borck10, Xun He11, Steven J. Luck12, Maro G. Machizawa13,14, Cyril Pernet15, Aina Puce16, Sidney Segalowitz17, Christine Rogers18,19, Muhammad Awais20, Claudio Babiloni21,22, Neil W. Bailey23, Sylvain Baillet18, Robert C. A. Bendall24, Daniel Brady25, Maria L. Bringas-Vega26, Niko Busch27, Ana Calzada-Reyes28, Armand Chatard29,30, Peter E. Clayson31, Michael X. Cohen32,33, Jonathan Cole34, Martin Constant35, Alexandra Corneyllie36, Damien Coyle37,38, Damian Cruse39, Ioannis Delis40, Arnaud Delorme41,42, Damien Fair43, Tiago H. Falk44, Matthias Gamer45, Giorgio Ganis46, Kilian Gloy47, Samantha Gregory48, Cameron Hassall49, Katherine Hiley1, Richard B. Ivry50, Karim Jerbi51,52, Michael Jenkins53, Jakob Kaiser54, Andreas Keil55, Robert T. Knight50, Silvia Kochen56, Boris Kotchoubey5, Olave Krigolson57, Nicolas Langer58, Heinrich R. Liesefeld59, Sarah Lippé51, Raquel E. London60, Annmarie MacNamara61, Scott Makeig62, Welber Marinovic63, Eduardo Martínez-Montes28, Aleya A. Marzuki64, Ryan K. Mathew65,66, Christoph Michel67, José d. R. Millán68,69, Mark Mon-Williams1, Lilia Morales-Chacón70, Richard Naar71, Gustav Nilsonne72, Guiomar Niso73, Erika Nyhus74, Robert Oostenveld75,72, Katharina Paul76, Walter Paulus77,78, Daniela M. Pfabigan79, Gilles Pourtois80, Stefan Rampp81,82, Manuel Rausch83,84, Kay Robbins85, Paolo M. Rossini86, Manuela Ruzzoli87,88, Barbara Schmidt89, Magdalena Senderecka90, Narayanan Srinivasan91, Yannik Stegmann45, Paul M. Thompson92, Mitchell Valdes-Sosa28, Melle J. W. van der Molen93, Domenica Veniero94, Edelyn Verona31, Bradley Voytek95, Dezhong Yao26,96, Alan C. Evans18,19, Pedro Valdes-Sosa26,28 1School of Psychology, University of Leeds, Leeds, UK, 2NIHR Leeds Biomedical Research Centre, Leeds, UK, 3Université de Montréal, Montreal, Canada, 4Sainte-Justine University Hospital Research Center, Montreal, Canada, 5Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany, 6Facultad de Psicología, Universidad Autonoma de Madrid, Madrid, España, 7Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK, 8Department of Psychology, Health and Social Care, Manchester Metropolitan University, Manchester, UK, 9Institut du Cerveau, CNRS, Sorbonne Université, Paris, France, 10Institute of Medical History and Science Research, Universität zu Lübeck, Lübeck, Germany, 11Department of Psychology, Bournemouth University, Bournemouth, UK, 12Center for Mind and Brain, UC Davis, California, USA, 13Xiberlinc Inc., Tokyo, Japan, 14Tokyo Medical and Dental University, Tokyo, Japan, 15Neurology and Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark, 16Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA, 17Department of Psychology, Brock University, Ontario, Canada, 18Montreal Neurological Institute, McGill University, Montreal, Canada, 19McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada, 20School of Computing Sciences, University of East Anglia, Norwich, UK, 21Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy, 22Hospital San Raffaele Cassino, Cassino, Frosinone, Italy, 23School of Medicine and Psychology, The Australian National University, Canberra, Australia, 24School of Health and Society, University of Salford, Salford, UK, 25School of Computer Science, University of Sheffield, Sheffield, UK, 26The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China, 27Institute of Psychology, University of Münster, Münster, Germany, 28Cuban Center for Neuroscience, Playa, Havana, Cuba, 29Universite ́ de Poitiers, Poitiers, France, 30Centre National de la Recherche Scientifique, France, 31Department of Psychology, University of South Florida, Florida, USA, 32Sincxpress Education, 33Donders Institute, Nijmegen, The Netherlands, 34University Hospital Dorset, NHS Foundation Trust, Poole, UK, 35Faculté de Psychologie et des Sciences de l’Education, Université de Genève, Geneva, Switzerland, 36Lyon Neurosience Research Centre, Lyon, France, 37The Bath Institute for the Augmented Human, University of Bath, Bath, UK, 38School of Computing, Engineering and Intelligent Systems, Ulster University, Ulster, UK, 39School of Psychology, University of Birmingham, Birmingham, UK, 40School of Biomedical Sciences, University of Leeds, Leeds, UK, 41Swartz Center of Computational Neuroscience, UC San Diego, California, USA, 42Centre de Recherche Cerveau et Cognition, Toulouse III University, Toulouse, France, 43Institute of Child Development, University of Minnesota, Minneapolis, Minnesota, USA, 44INRS-EMT, University de Quebec, Quebec, Canada, 45Department of Psychology, University of Würzburg, Würzburg, Germany, 46School of Psychology, University of Plymouth, Plymouth, UK, 47Institute of Psychology, University of Bremen, Bremen, Germany, 48School of Health & Society, University of Salford, Salford, UK, 49Department of Psychology, MacEwan University, Edmonton, Alberta, Canada, 50Department of Psychology, UC Berkeley, California, USA, 51Psychology Department, Université de Montréal, Montreal, Canada, 52UNIQUE, Quebec & Mila, Quebec, Canada, 53School of Medical and Life Sciences, Sunway University, Kuala Lumpur, Malaysia, 54Department of Psychology, Ludwig Maximilian University Munich, Munich, Germany, 55Department of Psychology, University of Florida, Florida, USA, 56Studies in Neuroscience and Complex Systems (ENyS), CONICET, Buenos Aires, Argentina, 57Centre for Biomedical Research, University of Victoria, Victoria, BC, Canada, 58Department of Psychology, University of Zurich, Zurich, Switzerland, 59Department of Psychology, University of Bremen, Bremen, Germany, 60Department of Experimental Psychology, Ghent University, Ghent, Belgium, 61Institute for Neuroscience, Texas A&M University, Texas, USA, 62Swartz Center for Computational Neuroscience, UC San Diego, California, USA, 63School of Psychology, Curtin University, Perth, Australia, 64Department for Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany, 65School of Medicine, University of Leeds, Leeds, UK, 66Leeds Teaching Hospitals NHS Trust, Leeds, UK, 67Departments of Clinical and Basic Neurosciences, University of Geneva, Geneva, Switzerland, 68Department of Electrical & Computer Engineering, The University of Texas at Austin, Austin, Texas, USA, 69Department of Neurology, The University of Texas at Austin, Austin, Texas, USA, 70International Center for Neurological Restoration, Havana, Cuba, 71Institute of Psychology, University of Tartu, Tartu, Estonia, 72Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden, 73Cajal Institute, CSIC, Madrid, España, 74Department of Psychology, Bowdoin College, Maine, USA, 75Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands, 76Faculty of Social Sciences, University Hamburg, Hamburg, Germany, 77Department of Neurology, Ludwig-Maximilians University Munich, Munich, Germany, 78University Medical Center Göttingen, Göttingen, Germany, 79Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway, 80Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium, 81Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany, 82University Hospital Halle (Saale), Halle, Germany, 83Faculty Society and Economics, Rhine-Waal University of Applied Sciences, Kleve, Germany, 84Department of Psychology, Catholic University of Eichstätt- Ingolstadt, Eichstätt, Germany, 85Department of Computer Science, The University of Texas at San Antonio, Texas, USA, 86Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy, 87Basque Center on Cognition Brain & Language, San Sebastian, España, 88Basque Foundation for Science, Bilbao, España, 89Institute of Psychosocial Medicine, Psychotherapy and Psychooncology, Jena University Hospital, Jena, Germany, 90Institute of Philosophy, Jagiellonian University, Kraków, Poland, 91Department of Cognitive Science, Indian Institute of Technology Kanpur, Kanpur, India, 92Keck School of Medicine, University of Southern California, California, USA, 93Institute of Psychology, Leiden University, Leiden, The Netherlands, 94School of Psychology, University of Nottingham, Nottingham, UK, 95Department of Cognitive Science, UC San Diego, California, USA, 96Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China †Correspondence can be addressed to Faisal Mushtaq (email: f.mushtaq@leeds.ac.uk) and Pedro Valdes-Sosa (email: pedro.valdes@neuroinformatics-collaboratory.org) Standfirst (360 characters): On the centenary of the first human EEG recording more than 500 experts reflect on the impact this discovery has had on our understanding of brain and behaviour. We document their priorities and call for collective action focusing on validity, democratization and responsibility, to realise the potential of EEG in science and society over the next 100 years. On 6 July 1924, psychiatrist Hans Berger found himself in an operating room in Jena, Germany, with the neurosurgeon Nikolai Guleke. Here, Berger made the first recording of spontaneous electrical activity from a human brain, which would lead to the development of modern electroencephalography (EEG; see Box 1). One hundred years later, we surveyed over 500 experts from over 50 countries (https://www.eeg100.org/survey), asking them to reflect on the impact of EEG on our understanding of brain function and dysfunction and where the community should prioritise efforts to maximise the impact of EEG. We also prompted them to speculate on EEG’s evolving role in neuroscience and society for the next 100 years. Our commentary draws upon these responses and ends with a call to action, pushing for collective action to realise EEG’s full potential. History & Impact In an era where physiologists worked at the level of cells and fibres, placing two electrodes on the brain’s surface seemed an absurd endeavour. Berger, engaged in a lifelong search for biomarkers of “mental energy”, was undeterred and, after years of toil, he made his breakthrough. While 1924 marked the year of discovery, a self-doubting Berger did not publicly reveal it to the world until 19291. In the intervening period, he undertook hundreds of experiments, extending his observations from direct recordings from the brain to the scalp. While the scientific community hesitated to embrace the discovery, the popular press wasted no time, coining the term “brain script” (“Hirnschrift”) to describe the waveforms captured by Berger’s galvanometer. Public discourse in the Weimar Republic reflected their excitement, with fantastical ideas on its potential—from telepathy to judging a horse’s temperament2. Perhaps, above all, the discovery brought an expectation that this unprecedented empirical access to a living human brain might help unravel the mysteries of the mind. It was, however, left for Lord Adrian, Nobel laureate and physiologist extraordinaire, to turn the scientific doubters into believers. Together with B.H.C. Matthews, Adrian replicated Berger’s experiments in 19342, lighting the torch for a new field of study. Soon after, new laboratories started pushing boundaries. The neural characteristics of sleep were quickly defined, with Einstein as a famous subject in these early studies2. Similarly, epilepsy, previously seen as a personality trait, was repositioned as a disorder of electrophysiological brain activity. This work, pioneered by William Lennox, and Erna and Frederic Gibbs, was a considerable success for developing biomarkers of neurological disorders3. Quantitative analysis of EEG (qEEG) was born when Mary Brazier and Norbert Wiener modelled the EEG as a stochastic process using analogue computers3. These approaches were quickly superseded by digital computers, which opened the way for evoked potentials, (time-)frequency analysis, artifact rejection, and progress on topics currently popular such as brain age and normative modelling. Reflecting on its history, our survey respondents reported that clinical diagnosis is where EEG has had its most significant impact. Today, EEG is supported by well-established scientific and professional societies that foster its use across the globe4. Indeed, it is often the only neuroimaging modality available in resource-limited clinical settings and remains the only imaging modality shown to be successful for mass screening of brain dysfunction5. Figure 1: An EEG recording in 2024. An illustration of a young participant wearing a modern wireless headset recording EEG outside of the laboratory in a school classroom setting. The signal displayed on the screen, repeated across rows, is an adaptation of an early recording taken by Hans Berger from his son Klaus1, showing a sinusoidal 10-Hz activity, which he referred to as the “alpha rhythm”. Box 1. What is EEG? Electroencephalography (EEG) is a non-invasive neuroimaging technique used to record electrical activity of the brain via electrodes placed on the scalp. The recorded signal, the electroencephalogram (which shares the acronym EEG), is the product of synchronized synaptic activity in populations of cortical neurons (pyramidal cells organized along cortical columns). Voltage fluctuations at each electrode site reflect a differential measurement between the active and reference electrodes that is amplified and recorded as an EEG trace. These electrical changes can be captured with high temporal resolution, offering a window into the time course of brain activity in the submillisecond range. EEG has proven particularly useful in a clinical setting because certain cases of abnormal brain function evoke relatively consistent EEG patterns that can be detected. Such applications have been facilitated by quantitative EEG (qEEG), the application of mathematical techniques to extract numerical features of the EEG trace to support signal interpretation. EEG traces provide a canonical test for epilepsy and can be used to identify sleep problems, determine whether the brain is alive or dead, or probe certain disorders of consciousness. Visual evoked potentials have been used in diagnosing multiple sclerosis, a disorder that leads to demyelination, and auditory evoked potentials detect abnormalities in the hearing of newborns. By time-locking the signal to a response or an external stimulus and averaging the signal over many trials, the neural activity that is specifically related to the sensory, motor, or cognitive event that evoked it can be extracted. This technique is regularly applied in studies monitoring brain maturation across development, in mental ill health and examining neural change following behavioural and pharmacological treatments. In academic research, EEG, through averaging the signal, and more recently, single trial analysis, has been used extensively to explore fundamental questions related to cognitive processing, including in the study of attention, emotion, memory, and decision-making. With its portability and low cost, EEG is increasingly being used in real-world settings, with communities and in environments where other neuroimaging tools are either too expensive or logistically impractical. Commercial applications leveraging EEG are also on the rise, making brain monitoring accessible to the public. Its integration with other technologies including artificial intelligence and virtual and augmented reality is creating new possibilities to interact with the digital and physical world. Advances in brain-computer interfaces (BCIs) show EEG can be used to control prosthetics and communication devices, to deliver neurofeedback training and promote physical rehabilitation. The Future To predict EEG’s impact over the next century, we generated a list of potential developments, breakthroughs, and achievements, covering what we assumed to be critical to progress through to the highly improbable. Our respondents gave an estimate of when (if at all) each statement would be fulfilled. Responses suggest that most predictions will be realised within the next couple of generations (Figure 2). Some near-term ambitions have already been fulfilled within specific quarters. For example, EEG contributes to the diagnosis of sleep disorders and there are established standards and automatic analysis approaches for some clinical applications3. Other predictions seem only a few years away. The idea that consumer-grade hardware will become common, and that EEG could be used for reliable detection of brain abnormalities and pharmacological interventions are ostensibly within reach. Personalised neuromodulation therapies also seem a promising avenue for improving brain function in disease and accelerating learning and skill acquisition in healthy individuals. Moreover, there is an expectation that progressive diseases including neurodegenerative dementias, which initially manifest at the synaptic level, will find in advanced EEG techniques a tool for early detection. As expected, the two boldest predictions, deciphering the contents of dreams and reading the contents of our long-term memory from EEG, elicited the most pessimistic responses. Figure 2: Predicting Future Milestones of EEG Survey respondents (n = 515, from 51 countries) with 6,685 years of collective experience rated when the EEG community might widely accept the listed statements as being achieved. Here we present rank-ordered median averages of all responses (error bars represent 95% confidence intervals of the mean). Statement labels are shortened for presentation (see6). Participants could opt out of making predictions if their uncertainty was too high. The percentage of response per statement is indicated by colour, ranging from teal (88%) to light grey (37%). Stratification of predictions by respondent characteristics is available through our web application (https://www.eeg100.org/survey/). 0 20 40 60 Can be used to read the content of long-term memories Can be used to read the content of dreams Used widely across the globe as an indicator of brain health Used to identify students in need of additional support Widely used as a lie detector Virtual environments adapt to EEG-informed cognitive states, emotions, & mental workload EEG-driven passive biometric authentication is integrated into the metaverse Interfaces with AR, VR, & metaverse platforms for seamless control of virtual environments Widely used with daily-use devices like smartphones & smartwatches Remote & telemedicine applications for neurological assessments & treatments Guidelines for responsible use in marketing, advertising, & entertainment industries Precise & individualized neuromodulation therapies for various neurological & psychiatric disorders Consumer-grade devices are widely used to monitor & enhance cognitive performance Integrated into workplace safety protocols in high-risk environments Capable of non-invasive assessment of brain health during prenatal & neonatal stages Primary tool for communication by individuals with severe motor disabilities or locked-in syndrome Machine learning-driven personalized treatment plans for mental health disorders Improvements in machine learning allow seamless decoding of cognitive states & emotions Accurate and efficient detection of early-stage neurodegenerative diseases Sufficiently robust & user-friendly that untrained individuals can collect high quality data Cost allows researchers & clinicians to access EEG in every part of the globe Widely agreed ontology mapping ERP components to cognitive processes Enables early detection & intervention for learning disabilities in children EEG-guided neurofeedback therapy standard treatment option for mental health disorders Ethical considerations on identity, autonomy & agency incorporated into BCI development EEG-BCIs widely adopted in gaming & VR Closed-loop EEG-BCI supports neuromodulation therapies Reliable tool for monitoring progression of traumatic brain injuries High quality EEG data can be acquired from everyone Scientific consensus on best practices for preprocessing data Widely agreed definitions on how to collect, analyse & interpret resting state Automatic preprocessing pipelines outperform human-in-the-loop workflows Used to study impact of pharmacological interventions Real-time, reliable detection of brain abnormalities such as seizures or tumours Portable, consumer-grade devices become widely available for personal use Routinely used in diagnosis & monitoring of sleep disorders Years Away Priorities Another objective of the survey was to identify the priorities of the EEG community for guiding future efforts. All our proposed priorities reached a median rating of at least moderately important (Figure 3). Of these, improvements in qEEG tools (artifact cleaning, recording hardware, and analysis software) ranked highest. Standardisation emerged as another urgent priority, with a need for consensus on the protocols used for data acquisition as well as for signal processing and data analysis in basic and clinical science. Hardware manufacturers and software developers have an important role to play here, with interoperability across devices and packages needed to support the adoption of standards. We propose that these priorities, together with the above predictions, should form a roadmap for the coming decades: technological advances will need to go hand in hand with community-agreed standards to optimise the future of EEG. Figure 3: Priorities for Progressing EEG. Participants rated how important major developments and advancements in various domains of EEG research would be to their work. The priority list is ordered by the frequency of “Extremely Important” ratings. 0 25 50 75 100 Hardware for non-expert users Integration with non-physiological modalities Software for data acquisition Solutions for archiving data Real-time EEG monitoring Integration with other neuroimaging modalities Real-time EEG analysis Electrode materials & designs Standardisation of data collection Solutions for sharing data Integration with other physiological measures Large-scale data acquisition Portable & wearable EEG systems Software for data analysis Standardisation of pre-processing Standardisation of data analysis Accuracy of source localisation Hardware for data acquisition Artifact detection & removal methods % Response Not at all Slightly Moderately Very Extremely A Call to Action In addition to rating priorities and estimating predictions, we also invited survey respondents to offer their insights through free-text responses. Their comments indicate a degree of optimism that emerging technologies are opening exciting new possibilities for EEG. Increasingly affordable hardware, coupled with advances in AI, virtual reality, and brain- computer interfacing, holds immense potential for advancing our understanding of brain- behaviour relationships. These technologies could also fundamentally transform our interactions with the physical and digital world and contribute to addressing the global burden of brain disorders7. However, there was also a sense of frustration with slow progress. Although our respondents were generally confident that EEG’s low cost, non- invasive nature, portability, and temporal resolution will secure its long-term future, it is striking that the development of EEG-based biomarkers for global brain health was seen as a more distant possibility. From the free-text responses, we also heard concerns—ranging from a lack of adherence to agreed standards and protocols for clinical and scientific practice to ethical questions created by novel commercial applications and the lure of ‘neuroenhancement’. We propose that for EEG to survive and thrive deep into the 22nd century and beyond, right now we must focus on the following aspects: (i) Validity, established by ensuring our work is robust, reliable, replicable, and as reproducible as possible in both basic research and clinical settings; (ii) Democratization, delivered through recognizing the importance of diversity of data to advance fundamental neuroscience and automation of processes to support the development of inclusive health policies; (iii) Responsibility, considering issues of equity in access, privacy, and sustainability. We elaborate on this manifesto below. Validity EEG has already proven its worth in several clinical settings. However, the lack of large open datasets annotated by experts has hindered the development and validation of new automated techniques and splintered the consolidation of research findings (but see8). In other fields such datasets have provided a foundation for machine learning and the application of AI—developments only starting in EEG. In research, large-scale investigations of EEG phenomena are underway9. Clinically oriented efforts, hampered by the progressive loss of clinician-academics specialising in EEG, are needed to generate large reproducible datasets that will be central to improving the diagnostic accuracy of new methods to address some of the highest priority items identified by our respondents. As such, it is surprising to see mixed perspectives towards open science practices. Solutions for sharing and archiving data ranked low but such efforts will be central to realising the most urgent priorities of improving methods and developing standards that are widely adopted. Democratization Despite EEG being the most widely used direct measure of brain function, it is still not accessible to most of the world4 and much of the scientific data come from a small number of countries and a small section of the population from therein. The EEG community, as elsewhere in science and society, is beginning to recognise the limitations that this lack of diversity brings. Recognizing the potential for bias, we sought to distribute the survey as widely as possible by extending beyond our personal networks, asking societies and device manufacturers to distribute the survey to their mailing lists to ensure broad and diverse participation. Despite this, the demographic of our final sample is noteworthy: most respondents work in universities in North America or Europe while lower and middle- income countries are poorly represented, participants in senior positions are generally male and only few participants are clinical workers or hard- and software engineers. If our sample is a reasonable reflection of the demographics of the EEG community, then such underrepresentation could have potentially negative consequences for the scientific and clinical importance of EEG, from understanding fundamental processes to interventions and evidence-based health-related policies10. The good news is that the field is well-positioned to tackle these challenges. Devices are becoming cheaper, more portable, and user-friendly. This is allowing scientists and clinicians to engage with communities traditionally excluded from EEG research. AI-driven automation, based on large representative datasets, could also help overcome the substantial barriers to We recommend: - Pooling resources to generate large annotated open data repositories to facilitate discovery science and improve diagnostic applications. - Continuing and accelerating community-driven efforts to implement standardised protocols for data collection, processing, and analysis to support reproducibility and improve replicability. accessing training and expertise to support interpretation in clinical settings. We believe these innovations will be important drivers in the acceptability and inclusivity of future applications of EEG and are excited by their potential to support our understanding of mechanisms of brain function in health and disease that represent all of society. The time is ripe for growing a more inclusive and diversified field of neuroscience. Responsibility Ongoing and potential future developments also raise new ethical questions that resonate with pressing societal challenges. EEG has significant promise as a tool for supporting the delivery of population brain health for all5. Moreover, our collective predictions suggest that EEG may become embedded in everyday commercial technology within a generation. Concerns around cognitive freedom and mental privacy must be addressed through regulation that prioritises protection from harm without limiting the benefits of open data11. With the expected proliferation of large-scale data that new low-cost and easily accessible consumer-oriented devices will bring, we must also consider the environmental costs of large-scale data acquisition (including waste management) and computing resources required for storing and processing those data and arrive at an approach that supports the long-term sustainability of our planet. We recommend: - Leveraging the affordability and portability of new EEG devices to work with minoritised communities. - Supporting international collaborations, networks and initiatives that can facilitate the global expansion of clinical and research activity; foster training programs, and resource sharing to build local expertise and infrastructure. Next Steps While it is unlikely that any of the current authors will be around to evaluate the success of our predictions in one hundred years, we trust that the present work and accompanying survey data will serve as a time capsule in the scientific record. At the same time, we recognise that these results capture only a partial picture of perspectives. We welcome more: as a homage to the years between the discovery and public release, the survey will remain open for the next 5 years and responses will be made publicly available. As we move through this fourth industrial revolution, we hope this will provide an outlet for new and seldom- heard voices to share their hopes, concerns, and priorities. More immediately, we invite the full spectrum of the neuroscience community—from academic, clinical and industry settings—to take up our call for action and commit to promoting robust, ethical, inclusive, and sustainable practices that will help realise a century of potential for EEG in science and society. We recommend: - Funders, institutes and individuals advocate for the development and use of environmentally friendly technologies and methods for data acquisition, storage, and processing, as well as for sharing and reuse of already recorded data to minimise EEG’s ecological footprint. - The development of ethical guidelines and regulations to support equitable access to brain data as well as the protection of sensitive personal information. Acknowledgements: This work was supported by the UK Research and Innovation Biotechnology and Biological Sciences Research Council (BB/X008428/1), the National Institute for Health and Care Research (NIHR) Leeds Biomedical Research Centre (NIHR203331) and the German Research Foundation (PA 4005/1-1) and is the result of a partnership between the #EEGManyLabs (https://eegmanylabs.com) project, EEGNet (Brain Canada Foundation #4940); and the Global Brain Consortium (https://globalbrainconsortium.org/). The latter is funded by Grant Y0301902610100201 of the University of Electronic Sciences and Technology of China, STI 2030-Major Projects Grant Number: 2022ZD0208500 and the Chengdu Science and Technology Bureau Program Grant Number: 2022GH02-00042- HZ. We would like to thank the organisations, societies and researchers supporting (see6 for full list) and all participants. References 1. Berger, H. Über das Elektrenkephalogramm des Menschen. Arch. Für Psychiatr. Nervenkrankh. 87, 527–570 (1929). 2. Borck, C. Brainwaves: A Cultural History of Electroencephalography. (Routledge, Abingdon, 2018). 3. Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields: Sixth Edition. (Wolters Kluwer Health, 2012). 4. Bringas-Vega, M. L., Michel, C. M., Saxena, S., White, T. & Valdes-Sosa, P. A. Neuroimaging and global health. NeuroImage 260, 119458 (2022). 5. World Health Organization. Measures of Early-Life Brain Health at Population Level. https://www.who.int/publications-detail-redirect/9789240084797 (2023). 6. Welke, D., Mushtaq, F. & van den Bosch, J. #EEG100: Supplementary Materials. osf.io/qv38p (2024). 7. Steinmetz, J. D. et al. Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. 23, 344–381 (2024). 8. Beniczky, S. et al. Standardized computer-based organized reporting of EEG: SCORE – Second version. Clin. Neurophysiol. 128, 2334–2346 (2017). 9. Pavlov, Y. G. et al. #EEGManyLabs: Investigating the replicability of influential EEG experiments. Cortex 144, 213–229 (2021). 10. Webb, E. K., Etter, J. A. & Kwasa, J. A. Addressing racial and phenotypic bias in human neuroscience methods. Nat. Neurosci. 25, 410–414 (2022). 11. Jwa, A. S. & Poldrack, R. A. Addressing privacy risk in neuroscience data: from data protection to harm prevention. J. Law Biosci. 9, lsac025 (2022). Springer.docx.pdf 100 years of EEG for brain and behaviour research Faisal Mushtaq, Dominik Welke, Anne Gallagher, Yuri G. Pavlov, Layla Kouara, Jorge Bosch-Bayard, Jasper J. F. van den Bosch, Mahnaz Arvaneh, Amy R. Bland, Maximilien Chaumon, Cornelius Borck, Xun He, Steven J. Luck, Maro G. Machizawa, Cyril Pernet, Aina Puce, Sidney Segalowitz, Christine Rogers, Muhammad Awais, Claudio Babiloni, Neil W. Bailey, Sylvain Baillet, Robert C. A. Bendall, Daniel Brady, Maria L. Bringas-Vega, Niko Busch, Ana Calzada-Reyes, Armand Chatard, Peter E. Clayson, Michael X. Cohen, Jonathan Cole, Martin Constant, Alexandra Corneyllie, Damien Coyle, Damian Cruse, Ioannis Delis, Arnaud Delorme, Damien Fair, Tiago H. Falk, Matthias Gamer, Giorgio Ganis, Kilian Gloy, Samantha Gregory, Cameron Hassall, Katherine Hiley, Richard B. Ivry, Karim Jerbi, Michael Jenkins, Jakob Kaiser, Andreas Keil, Robert T. Knight, Silvia Kochen, Boris Kotchoubey, Olave Krigolson, Nicolas Langer, Heinrich R. Liesefeld, Sarah Lippé, Raquel E. London, Annmarie MacNamara, Scott Makeig, Welber Marinovic, Eduardo Martínez-Montes, One hundred years of EEG for brain and behaviour research