Introduction:Current brain-computer interfaces (BCIs) primarily rely on visual feedback. However, visual
feedback may not be sufficient for applications such as movement restoration, where somatosensory
feedback plays a crucial role. For electrocorticography (ECoG)-based BCIs, somatosensory feedback can
be elicited by cortical surface electro-stimulation [1]. However, simultaneous cortical stimulation and
recording is challenging due to stimulation artifacts. Depending on the orientation of stimulating
electrodes, their distance to the recording site, and the stimulation intensity, these artifacts may
overwhelm the neural signals of interest and saturate the recording bioamplifiers, making it impossible
to recover the underlying information [2]. To understand how these factors affect artifact propagation,
we performed a preliminary characterization of ECoG signals during cortical
stimulation.Materials/Methods/ResultsECoG electrodes were implanted in a 39-year old epilepsy
patient as shown in Fig. 1. Pairs of adjacent electrodes were stimulated as a part of language cortical
mapping. For each stimulating pair, a charge-balanced biphasic square pulse train of current at 50 Hz
was delivered for five seconds at 2, 4, 6, 8 and 10 mA. ECoG signals were recorded at 512 Hz. The signals
were then high-pass filtered (≥1.5 Hz, zero phase), and the 5-second stimulation epochs were
segmented. Within each epoch, artifact-induced peaks were detected for each electrode, except the
stimulating pair, where signals were clipped due to amplifier saturation. These peaks were phase-locked
across electrodes and were 20 ms apart, thus matching the pulse train frequency. The response was
characterized by calculating the median peak within the 5-second epochs. Fig. 1 shows a representative
response of the right temporal grid (RTG), with the stimulation channel at RTG electrodes 14 and 15. It
also shows a hypothetical amplifier saturation contour of an implantable, bi-directional, ECoG-based BCI
prototype [2], assuming the supply voltage of 2.2 V and a gain of 66 dB. Finally, we quantify the worstcase
scenario by calculating the largest distance between the saturation contour and the midpoint of
each stimulating channel.Discussion:Our results indicate that artifact propagation follows a dipole
potential distribution with the extent of the saturation region (the interior of the white contour)
proportional to the stimulation amplitude. In general, the artifacts propagated farthest when a 10 mA
current was applied with the saturation regions extending from 17 to 32 mm away from the midpoint of
the dipole. Consistent with the electric dipole model, this maximum spread happened along the
direction of the dipole moment. An exception occurred at stimulation channel RTG11-16, for which an
additional saturation contour emerged away from the dipole contour (not shown), extending the
saturation region to 41 mm. Also, the worst-case scenario was observed at 6 mA stimulation amplitude.
This departure could be a sign of a nonlinear, switch-like behavior, wherein additional conduction
pathways could become engaged in response to sufficiently high stimulation.Significance:While ECoG
stimulation is routinely performed in the clinical setting, quantitative studies of the resulting signals are
lacking. Our preliminary study demonstrates that stimulation artifacts largely obey dipole distributions,
suggesting that the dipole model could be used to predict artifact propagation. Further studies are
necessary to ascertain whether these results hold across other subjects and combinations of
stimulation/recording grids. Once completed, these studies will reveal practical design constraints for
future implantable bi-directional ECoG-based BCIs. These include parameters such as the distances
between and relative orientations of the stimulating and recording electrodes, the choice of the
stimulating electrodes, the optimal placement of the reference electrode, and the maximum stimulation
amplitude. These findings would also have important implications for the design of custom, low-power bioamplifiers for implantable bi-directional ECoG-based BCIs.References:[1] Hiremath, S. V., et al.
"Human perception of electrical stimulation on the surface of somatosensory cortex." PloS one 12.5
(2017): e0176020.[2] Rouse, A. G., et al. "A chronic generalized bi-directional brain-machine interface."
Journal of Neural Engineering 8.3 (2011): 036018
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Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance
Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.
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- Award ID(s):
- 1935860
- NSF-PAR ID:
- 10341423
- Date Published:
- Journal Name:
- IEEE Transactions on Neural Networks and Learning Systems
- ISSN:
- 2162-237X
- Page Range / eLocation ID:
- 1-14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TNNLS.2022.3174528
