The Experts below are selected from a list of 3282 Experts worldwide ranked by ideXlab platform
Raphael Zimmermann - One of the best experts on this subject based on the ideXlab platform.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Background: Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods: Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxyand deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. Results: fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions: This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
Martin Wolf - One of the best experts on this subject based on the ideXlab platform.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Background: Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods: Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxyand deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. Results: fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions: This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
Andres Monroyhernandez - One of the best experts on this subject based on the ideXlab platform.
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significant otter understanding the role of Biosignals in communication
arXiv: Human-Computer Interaction, 2021Co-Authors: Fannie Liu, Geoff Kaufman, Laura Dabbish, Chunjong Park, Yu Jiang Tham, Tsungyu Tsai, Andres MonroyhernandezAbstract:With the growing ubiquity of wearable devices, sensed physiological responses provide new means to connect with others. While recent research demonstrates the expressive potential for Biosignals, the value of sharing these personal data remains unclear. To understand their role in communication, we created Significant Otter, an Apple Watch/iPhone app that enables romantic partners to share and respond to each other's Biosignals in the form of animated otter avatars. In a one-month study with 20 couples, participants used Significant Otter with Biosignals sensing OFF and ON. We found that while sensing OFF enabled couples to keep in touch, sensing ON enabled easier and more authentic communication that fostered social connection. However, the addition of Biosignals introduced concerns about autonomy and agency over the messages they sent. We discuss design implications and future directions for communication systems that recommend messages based on Biosignals.
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animo sharing Biosignals on a smartwatch for lightweight social connection
arXiv: Human-Computer Interaction, 2019Co-Authors: Mario Esparza, Maria Pavlovskaia, Geoff Kaufman, Laura Dabbish, Andres MonroyhernandezAbstract:We present Animo, a smartwatch app that enables people to share and view each other's Biosignals. We designed and engineered Animo to explore new ground for smartwatch-based Biosignals social computing systems: identifying opportunities where these systems can support lightweight and mood-centric interactions. In our work we develop, explore, and evaluate several innovative features designed for dyadic communication of heart rate. We discuss the results of a two-week study (N=34), including new communication patterns participants engaged in, and outline the design landscape for communicating with Biosignals on smartwatches.
Robert Riener - One of the best experts on this subject based on the ideXlab platform.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Background: Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods: Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxyand deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. Results: fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions: This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
Laura Marchalcrespo - One of the best experts on this subject based on the ideXlab platform.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Background: Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods: Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxyand deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. Results: fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions: This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
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detection of motor execution using a hybrid fnirs biosignal bci a feasibility study
Journal of Neuroengineering and Rehabilitation, 2013Co-Authors: Raphael Zimmermann, Laura Marchalcrespo, Janis Edelmann, Marie-christine Fluet, Robert Riener, Olivier Lambercy, Martin WolfAbstract:Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and Biosignals combined. fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when Biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired Biosignals. Combining fNIRS data with Biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.