An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic
Brain-computer interfaces (BCIs) still face many challenges to step out of laboratories to be used in real-life applications. A key one persists in the high performance control of diverse effectors for complex tasks, using chronic and safe recorders. This control must be robust over time and of high decoding performance without continuous recalibration of the decoders. In the article, asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) implant is demonstrated. For this purpose, an adaptive online tensor-based decoder: the Recursive Exponentially Weighted Markov-Switching multi-Linear Model (REW-MSLM) was developed. We demonstrated over a period of 6 months the stability of the 8-dimensional alternative bimanual control of the exoskeleton and its virtual avatar using REW-MSLM without recalibration of the decoder.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderSimilar Papers 제목 키워드 기반
Federated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective
Invasive and non-invasive neural interfaces hold promise as high-bandwidth input devices for next-generation technologies. However, neural signals inherently encode sensitive information about an individual's identity an…
Federated LearningPrivacy PreservingAn energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface
The number of simultaneously recorded neurons follows an exponentially increasing trend in implantable brain-machine interfaces (iBMIs). Integrating the neural decoder in the implant is an effective data compression meth…
Reinforcement LearningA multi-agent control framework for co-adaptation in brain-computer interfaces
In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response. Feedback to the user provides information which permits the neural tuning to …
Brain Computer InterfaceDecodermaxVSTAR: Maximally Adaptive Vision-Guided CSI Sensing with Closed-Loop Edge Model Adaptation for Robust Human Activity Recognition
WiFi Channel State Information (CSI)-based human activity recognition (HAR) provides a privacy-preserving, device-free sensing solution for smart environments. However, its deployment on edge devices is severely constrai…
Human Activity RecognitionCo-adaptive learning over a countable space
Co-adaptation is a special form of on-line learning where an algorithm $\mathcal{A}$ must assist an unknown algorithm $\mathcal{B}$ to perform some task. This is a general framework and has applications in recommendation…
DecoderRecommendation Systems