Publications related to Brain-reading

Superposition model for steady state visually evoked potentials

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Jaiber Evelio Cardona Aristizabal, Wilfredo Alfonso Morales

Steady State Visually Evoked Potentials (SSVEP) are signals produced in the occipital part of the brain when someone gaze a light flickering at a fixed frequency. These signals have been used for Brain Machine Interfacing (BMI), where one or more stimuli a ...

2016

Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Hannes Bleuler, Solaiman Shokur, Simon Gallo, Miguel Nicolelis

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. ...

Nature Publishing Group2016

Quantifying the role of motor imagery in brain-machine interfaces

Olaf Blanke, Hannes Bleuler, Andrea Serino, Michela Bassolino, Silvia Marchesotti

Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related ...

2016

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

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil

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 c ...

Nature Publishing Group2015

Quantifying electrode reliability during Brain-Computer Interface operation

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Serafeim Perdikis, Hesam Sagha

One of the problems of non-invasive Brain-Computer Interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator’s attention since raw signals are not usually con ...

Institute of Electrical and Electronics Engineers2015

Quantifying the time for accurate EEG decoding of single value-based decisions

Ricardo Andres Chavarriaga Lozano, Athina Tzovara

Background Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. ...

Elsevier2015

Moving Brain-Controlled Devices Outside the Lab: Principles and Applications

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Robert Leeb, Inaki Asier Iturrate Gil, Serafeim Perdikis

This chapter provides an overview of the functionality and the underlying principles of the brain-computer interfaces (BCI) developed by the Chair in Non-Invasive Brain-Machine Interface (CNBI) of the Swiss Federal Institute of Technology (EPFL), as well a ...

Springer Netherlands2015

EEG-based decoding of error-related brain activity in a real-world driving task

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Huaijian Zhang, Lucian Andrei Gheorghe, Zahra Khaliliardali

Objectives. Recent studies have started to explore the implementation of brain–computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be us ...

Institute of Physics2015

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Brain-reading

Superposition model for steady state visually evoked potentials

Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Quantifying the role of motor imagery in brain-machine interfaces

Local field potentials in primate motor cortex encode grasp kinetic parameters

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

Quantifying electrode reliability during Brain-Computer Interface operation

Brain-Machine Interfaces: The Perception-Action Closed Loop

Quantifying the time for accurate EEG decoding of single value-based decisions

Moving Brain-Controlled Devices Outside the Lab: Principles and Applications

EEG-based decoding of error-related brain activity in a real-world driving task

Graph Chatbot

Chat with Graph Search

Quantifying electrode reliability during Brain-Computer Interface operation

Quantifying the time for accurate EEG decoding of single value-based decisions

Moving Brain-Controlled Devices Outside the Lab: Principles and Applications

EEG-based decoding of error-related brain activity in a real-world driving task

Brain-Machine Interfaces: The Perception-Action Closed Loop

Superposition model for steady state visually evoked potentials

Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Local field potentials in primate motor cortex encode grasp kinetic parameters

Quantifying the role of motor imagery in brain-machine interfaces

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