  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.
