brain–machine interface

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Publications 9

Can you feel what I feel?

Publication date: September 23, 2016

Link: http://science.sciencemag.org/content/353/6306/twil

Keywords: brain–machine interface, human-robot interaction, machine learning, wearable robotics

Prof. Robert Riener NCCR Co-Director & Professor Management Team and Sensory-Motor Systems Lab (SMS), ETH Zurich riener@hest.ethz.ch +41 44 632 66 79

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A Brain-Controlled Exoskeleton with Cascaded Event-Related Desynchronization Classifiers

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Authors:
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Lee, Kyuhwa; Liu, Dong; Perroud, Laetitia; Chavarriaga, Ricardo; Millán, José del R.

This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two different mental states, which is applied in a cascaded manner to efficiently control the exoskeleton in three different directions: walk front, turn left and turn right. This is realized by first classifying the user’s intention to walk front or change the direction. If the user decides to change the direction, a subsequent classification is performed to decide turn left or right. The user’s mental command is conditionally executed considering the possibility of obstacle collision. All five subjects were able to successfully complete the 3-way navigation task using brain signals while mounted in the exoskeleton. We observed on average 10.2% decrease in overall task completion time compared to the baseline protocol.

Posted on: August 31, 2016

Keywords: BCI, bmi, brain–computer interface, brain–machine interface, eeg, exoskeleton

Brain-actuated gait trainer with visual and proprioceptive feedback

Authors:
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Liu, Dong; Chen, Weihai; Lee, Kyuhwa; Chavarriaga, Ricardo; Bouri, Mohamed; Pei, Zhongcai; Millán, José del R.

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension. Approach. We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects. Main Results. (i) For real-time classification, the average accuracy was 62.33 ± 4.95% and 63.89 ± 6.41% for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback (69.45 ± 9.95%) was significantly better than with visual feedback (62.89 ± 9.20%), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn. Significance. The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that proprioceptive feedback has an advantage over visual feedback, which could be used to improve robot-assisted strategies for motor training and functional recovery.

Posted on: July 10, 2017

Keywords: brain–machine interface, electroencephalography, lower-limb rehabilitation, proprioceptive feedback

Decoding Fast-Paced Error-Related Potentials in Monitoring Protocols

Authors:
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Chavarriaga, Ricardo; Iturrate, Inaki; Wannebroucq, Quentin Olivier Pierre; Millán, José del R.

Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject’s perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities. However, previously reported approaches are limited by the use of long inter-stimulus intervals (ISI>2s). In this work we assess if it is possible to overcome this limitation. Our results show that it is possible to decode error-related potentials elicited by stimuli presented with ISIs lower than 1s without decrease in performance. Furthermore, the increase in the presentation rate did not increase the subject workload. This suggests that the presentation rate for ErrP-based BMI protocols using serial monitoring paradigms can be substantially increased with respect to previous works.

Posted on: June 16, 2015

Keywords: brain–machine interface, eeg, error-related potentials

Errare machinale est: The use of error-related potentials in brain-machine interfaces

Authors:
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Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José del R.

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments towards this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine’s actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches.

Posted on: July 4, 2014

Keywords: brain–machine interface, eeg, error-related potentials, hybrid bci, neuroprosthesis, reinforcement learning

Harnessing prefrontal cognitive signals for brain-machine interfaces

Authors:
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Min, Byoung-Kyong; Chavarriaga, Ricardo; Millán, José del R.

Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive. Thus, this paper identifies the prefrontal brain area as a key target region for future BMIs, given its involvement in higher-order, goal-oriented cognitive processes.

Posted on: March 27, 2017

Keywords: brain–machine interface, cognition, electroencephalography, prefrontal cortex

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

Authors:
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Corbet, Tiffany; Iturrate, Inaki; Pereira, Michael Eric Anthony; Perdikis, Serafeim; Millán, José del R.

Posted on: April 12, 2018

Keywords: brain–machine interface, eeg imaging, kinesthetic imagery, motor imagery, sensory electrical stimulation

Spatial filters yield stable features for error-related potentials across conditions

Authors:
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Iwane, Fumiaki; Chavarriaga Lozano, Ricardo; Iturrate, Inaki; Millán, José del R.

Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration processes, thus increasing their usability. Previous studies have suggested that ErrP signals are rather stable across recording sessions. Also, studies using protocols of serial stimuli presentation show that these potentials do not change significantly with the presentation rate. Here we complement these studies by analysing the decoding generalisation capabilities. Using data from monitoring experiments, we evaluate how much the performance degrades when tested in a condition different than the one the decoder was trained with. Moreover, we compare different spatial filtering techniques to see which preprocessing steps yield less-sensitive features for ErrP decoding.

Posted on: December 20, 2016

Keywords: brain–machine interface, eeg, error-related potentials

Workshops of the Sixth International Brain-Computer Interface Meeting: Brain-Computer Interfaces Past, Present, and Future

Authors:
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Guger, Christoph; Ziat, Mounia; Zander, Thorsten O.; Taylor, Denise; Tangermann, Michael; Soria-Frisch, Aureli; Simeral, John; Scherer, Reinhold; Rupp, Rüdiger; Ruffini, Giulio; Robinson, Douglas K.R.; Ramsey, Nick F.; Nijholt, Anton; Müller-Putz, Gernot; McFarland, Denis J.; Mattia, Donatella; Lance, Brent J.; Kindermanns, Pieter-Jan; Iturrate, Inaki; Herff, Christian; Gupta, Disha; Do, An H.; Collinger, Jennifer L.; Chavarriaga, Ricardo; Chase, Steven M.; Bleichner, Martin G.; Batista, Aaron; Anderson, Charles W.

The Sixth International Brain-Computer Interface (BCI) Meeting was held May 30-June 3rd, 2016 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Posted on: January 5, 2017

Keywords: brain–computer interface, brain–machine interface, conference, neuroprosthetics

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Authors:
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Huggins, Jane E.; Guger, Christoph; Ziat, Mounia; Zander, Thorsten O.; Taylor, Denise; Tangermann, Michael; Soria-Frisch, Aureli; Simeral, John; Scherer, Reinhold; Rupp, Rüdiger; Ruffini, Giulio; Robinson, Douglas K. R.; Ramsey, Nick F.; Nijholt, Anton; Müller-Putz, Gernot; McFarland, Dennis J.; Mattia, Donatella; Lance, Brent J.; Kindermans, Pieter-Jan; Iturrate, Inaki; Herff, Christian; Gupta, Disha; Do, An H.; Collinger, Jennifer L.; Chavarriaga, Ricardo; Chase, Steven M.; Bleichner, Martin G.; Batista, Aaron; Anderson, Charles W.; Aarnoutse, Erik J.

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive or non-invasive methods. The key point is that the signals that are interpreted come directly from the brain, bypassing sensorimotor output channels that may or may not have impaired function. This paper provides a concise glimpse of the breadth of BCI research and development topics covered by the workshops of the 6th International Brain–Computer Interface Meeting.

Posted on: January 31, 2017