José del R. Millán

The University of Texas at Austin

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Iñaki Iturrate

National Institutes of Health

Ricardo Chavarriaga

ZHAW Zurich University of Applied Sciences

Robert T. Knight

University of California, Berkeley

Fumiaki Iwane

École Polytechnique Fédérale de Lausanne

Stéphanie Martin

University of North Carolina at Chapel Hill

Serafeim Perdikis

University of Essex

Gerwin Schalk

Albany Medical Center Hospital

Dimitri Van De Ville

École Polytechnique Fédérale de Lausanne

Brian N. Pasley

University of California, Berkeley

Olaf Blanke

École Polytechnique Fédérale de Lausanne

Cooperative Institutions

École Polytechnique Fédérale de Lausanne

Centre National de la Recherche Scientifique

Otto-von-Guericke University Magdeburg

University of Leeds

The University of Texas at Austin

University Hospital Magdeburg

University of Geneva

University of Electronic Science and Technology of China

Inserm

Cuban Neuroscience Center

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A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface

Biosensors and Bioelectronics (2022)

Ju-Chun Hsieh Hussein Alawieh Yang Li Fumiaki Iwane Linran Zhao

Functional electrical stimulation

Interface (matter)

10.1016/j.bios.2022.114756

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Citations (39)

Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control

Scientific Reports (2015)

Iñaki Iturrate Ricardo Chavarriaga Luis Montesano Javier Mínguez José del R. Millán

Abstract Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user’s training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.

Neuroprosthetics

10.1038/srep13893

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Citations (141)

Correction: Corrigendum: Word pair classification during imagined speech using direct brain recordings

Scientific Reports (2017)

Stéphanie Martin Peter Brunner Iñaki Iturrate José del R. Millán Gerwin Schalk

Scientific Reports 6: Article number: 25803; published online: 11 May 2016; updated: 23 March 2017 This Article contains errors in the order of Figures 1, 2 and 3. Figure 1 was published as Figure 2, Figure 2 was published as Figure 3 and Figure 3 was published as Figure 1. The correct Figures appear below.

Word list

10.1038/srep44509

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Análisis Neotectónico del Sistema de Falla La Cantera, Precordillera Central, San Juan, Argentina. Su evaluación como fuente sismogénica potencia

Laura P. Perucca María Romina Onorato José del R. Millán Ariel Bustos Nicolás Vargas

Fil: Perucca, Laura Patricia A.. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Fisicas y Naturales. Instituto de Geologia Dr. Emiliano Aparicio. Gabinete de Neotectonica y Geomorfologia; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico San Juan. Centro de Investigaciones de la Geosfera y Biosfera; Argentina

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EEG topographies provide subject-specific correlates of motor control

Scientific Reports (2017)

Elvira Pirondini Martina Coscia Jesús Minguillón José del R. Millán Dimitri Van De Ville

Abstract Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-established role during resting state, their implication in the generation of motor behavior is debated. Evidence of such a functional role of spontaneous brain activity would provide support for the design of novel and sensitive biomarkers in neurological disorders. Here we examined whether and to what extent intrinsic brain activity contributes and plays a functional role during natural motor behaviors. For this we first extracted subject-specific EEG microstates and muscle synergies during reaching-and-grasping movements in healthy volunteers. We show that, in every subject, well-known resting-state microstates persist during movement execution with similar topographies and temporal characteristics, but are supplemented by novel task-related microstates. We then show that the subject-specific microstates’ dynamical organization correlates with the activation of muscle synergies and can be used to decode individual grasping movements with high accuracy. These findings provide first evidence that spontaneous brain activity encodes detailed information about motor control, offering as such the prospect of a novel tool for the definition of subject-specific biomarkers of brain plasticity and recovery in neuro-motor disorders.

Motor Control

10.1038/s41598-017-13482-1

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Citations (42)

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Scientific Reports (2020)

Camille Jeunet Luca Tonin Louis Albert Ricardo Chavarriaga Benoît Bideau

Abstract Advances in sports sciences and neurosciences offer new opportunities to design efficient and motivating sport training tools. For instance, using NeuroFeedback (NF), athletes can learn to self-regulate specific brain rhythms and consequently improve their performances. Here, we focused on soccer goalkeepers’ Covert Visual Spatial Attention (CVSA) abilities, which are essential for these athletes to reach high performances. We looked for Electroencephalography (EEG) markers of CVSA usable for virtual reality-based NF training procedures, i.e., markers that comply with the following criteria: (1) specific to CVSA, (2) detectable in real-time and (3) related to goalkeepers’ performance/expertise. Our results revealed that the best-known EEG marker of CVSA—increased α -power ipsilateral to the attended hemi-field— was not usable since it did not comply with criteria 2 and 3. Nonetheless, we highlighted a significant positive correlation between athletes’ improvement in CVSA abilities and the increase of their α -power at rest. While the specificity of this marker remains to be demonstrated, it complied with both criteria 2 and 3. This result suggests that it may be possible to design innovative ecological training procedures for goalkeepers, for instance using a combination of NF and cognitive tasks performed in virtual reality.

Covert

USable

10.1038/s41598-020-58533-2

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Citations (20)

One hundred years of EEG for brain and behaviour research

Nature Human Behaviour (2024)

Faisal Mushtaq Dominik Welke Ann Gallagher Yuri G. Pavlov Layla Kouara

10.1038/s41562-024-01941-5

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Citations (8)

Word pair classification during imagined speech using direct brain recordings

Scientific Reports (2016)

Stéphanie Martin Peter Brunner Iñaki Iturrate José del R. Millán Gerwin Schalk

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.

10.1038/srep25803

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Citations (142)

Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

Nature Communications (2018)

Andrea Biasiucci R. Leeb Iñaki Iturrate Serafeim Perdikis Abdul Al-Khodairy

Abstract Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6–12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI–FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways.

Functional electrical stimulation

Functional movement

Stroke

Brain stimulation

Stroke Recovery

Motor Imagery

10.1038/s41467-018-04673-z

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Citations (431)

Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging

Proceedings of the National Academy of Sciences (2020)

Michael Pereira Nathan Faivre Iñaki Iturrate Marco Wirthlin Luana Serafini

The human capacity to compute the likelihood that a decision is correct—known as metacognition—has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacognition by analyzing neural correlates of confidence with multimodal imaging. Healthy volunteers reported their confidence in the accuracy of decisions they made or decisions they observed. We found better metacognitive performance for committed vs. observed decisions, indicating that committing to a decision may improve confidence. Relying on concurrent electroencephalography and hemodynamic recordings, we found a common correlate of confidence following committed and observed decisions in the inferior frontal gyrus and a dissociation in the anterior prefrontal cortex and anterior insula. We discuss these results in light of decisional and postdecisional accounts of confidence and propose a computational model of confidence in which metacognitive performance naturally improves when evidence accumulation is constrained upon committing a decision.

Inferior frontal gyrus

Low Confidence

10.1073/pnas.1918335117

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Citations (66)