  Objective. Brain-computer interfaces (BCIs) create a new communication
pathway between the brain and an effector without neuromuscular activation. BCI
experiments highlighted high intra and inter-subjects variability in the BCI
decoders. Although BCI model is generally relying on neurological markers
generalizable on the majority of subjects, it requires to generate a wide range
of neural features to include possible neurophysiological patterns. However,
the processing of noisy and high dimensional features, such as brain signals,
brings several challenges to overcome such as model calibration issues, model
generalization and interpretation problems and hardware related obstacles.
Approach. An online adaptive group-wise sparse decoder named Lp-Penalized
REW-NPLS algorithm (PREW-NPLS) is presented to reduce the feature space
dimension employed for BCI decoding. The proposed decoder was designed to
create BCI systems with low computational cost suited for portable applications
and tested during offline pseudo-online study based on online closed-loop BCI
control of the left and right 3D arm movements of a virtual avatar from the
ECoG recordings of a tetraplegic patient.
  Main results. PREW-NPLS algorithm highlight at least as good decoding
performance as REW-NPLS algorithm. However, the decoding performance obtained
with PREW-NPLS were achieved thanks to sparse models with up to 64% and 75% of
the electrodes set to 0 for the left and right hand models respectively using
L1-PREW-NPLS.
  Significance. The designed solution proposed an online incremental adaptive
algorithm suitable for online adaptive decoder calibration which estimate
sparse decoding solutions. The PREW-NPLS models are suited for portable
applications with low computational power using only small number of electrodes
with degrading the decoding performance.
