Idle state classification using spiking activity and local field potentials in a brain computer interface

Abstract

Previous studies of intracortical brain-computer interfaces (BCIs) have often focused on or compared the use of spiking activity and local field potentials (LFPs) for decoding kinematic movement parameters. Conversely, using these signals to detect the initial intention to use a neuroprosthetic device or not has remained a relatively understudied problem. In this study, we examined the relative performance of spiking activity and LFP signals in detecting discrete state changes in attention regarding a user's desire to actively control a BCI device. Preliminary offline results suggest that the beta and high gamma frequency bands of LFP activity demonstrated a capacity for discriminating idle/active BCI control states equal to or greater than firing rate activity on the same channel. Population classifier models using either signal modality demonstrated an indistinguishably high degree of accuracy in decoding rest periods from active BCI reach periods as well as other portions of active BCI task trials. These results suggest that either signal modality may be used to reliably detect discrete state changes on a fine time scale for the purpose of gating neural prosthetic movements.

Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface

“…As a result, the nearby neurons would have a dampened ability to generate action potentials that can be recorded by the electrode. Despite these intrinsic network changes, a multitude of BCI studies, as well as our studies in rodent visual cortex, indicate that chronically implanted intracortical electrodes are still capable of recording and extracting functional information 7–9,39,41,55,111–115 . Nevertheless, large variability in device performance is well documented 7–9,29,93,116 .…”

Cyclic, condition-independent activity in primary motor cortex predicts corrective movement behavior

“…When algorithms for updating BCI decoders consider the change in movement direction for corrective movements, it is assumed the intended path is updated continuously (Gilja et al, 2012; Shanechi et al, 2016). Experiments have suggested that BCI control can be improved with two states: active control and rest (Kim et al, 2011; Williams et al, 2013, 2016; Sachs et al, 2016). The ability to predict the phase of a movement—like CIφ presented here—might further allow for much better prediction of when the subject intends to move, contact the target, and make a corrective movement.…”

“…When algorithms for updating BCI decoders consider the change in movement direction for corrective movements, it typically has been assumed the intended path is updated continuously (Gilja et al, 2012; Shanechi et al, 2016). Experiments have suggested that BCI control can be improved with two states: active control and rest (Kim et al, 2011; Williams et al, 2013, 2016; Sachs et al, 2016). Our results suggest that computing the phase of cyclic, condition-independent neural activity with CIφ (Fig.…”

“…Experiments have suggested that BCI control can be improved with two states: active control and rest (Kim et al, 2011;Williams et al, 2013Williams et al, , 2016Sachs et al, 2016). The ability to predict the phase of a movement-like CIφ presented here-might further allow for much better prediction of when the subject intends to move, contact the target, and make a corrective movement.…”

Brain-Computer Interfaces for upper limb motor rehabilitation of stroke patients

“…BCI are systems that translate brain signals into commands to control external devices [1] . Brain signals can be acquired by means of different 207 techniques as magnetoencephalography (MEG) [38] , near-infrared spectroscopy (NIRS) [39] , electrocorticography (ECoG) [40] , local field potentials (LFP) recordings [41] , electroencephalography (EEG) [42] , among others. MEG has the best compromise between time and spatial resolution, however, it also the most complex acquisition modality for BCI and the least available for practical applications.…”