BCI-controlled wheelchairs: end-users' perceptions, needs, and expectations, an interview-based study.

PURPOSE: Brain-computer interface (BCI)-controlled wheelchairs have the potential to improve the independence of people with mobility impairments. The low uptake of BCI devices has been linked to a lack of knowledge among researchers of the needs of end-users that should influence BCI development. MATERIALS AND METHODS: This study used semi-structured interviews to learn about the perceptions, needs, and expectations of spinal cord injury (SCI) patients with regards to a BCI-controlled wheelchair. Topics discussed in the interview include: paradigms, shared control, safety, robustness, channel selection, hardware, and experimental design. The interviews were recorded and then transcribed. Analysis was carried out using coding based on grounded theory principles. RESULTS: The majority of participants had a positive view of BCI-controlled wheelchair technology and were willing to use the technology. Core issues were raised regarding safety, cost and aesthetics. Interview discussions were linked to state-of-the-art BCI technology. The results challenge the current reliance of researchers on the motor-imagery paradigm by suggesting end-users expect highly intuitive paradigms. There also needs to be a stronger focus on obstacle avoidance and safety features in BCI wheelchairs. Finally, the development of control approaches that can be personalized for individual users may be instrumental for widespread adoption of these devices. CONCLUSIONS: This study, based on interviews with SCI patients, indicates that BCI-controlled wheelchairs are a promising assistive technology that would be well received by end-users. Recommendations for a more person-centered design of BCI controlled wheelchairs are made and clear avenues for future research are identified.IMPLICATIONS FOR REHABILITATIONBrain-computer interface (BCI)-controlled wheelchairs are a promising assistive technology. The majority of participants had positive views of these devices and showed a willingness to try out such a device.Concerns centered on safety, cost and aesthetics.Integrated obstacle avoidance was viewed positively by most of the participants, but some had a negative view, expressing concerns about its safety, or reduced autonomy. Customizable control options should thus be integrated to cater for the needs of different individuals.

Monopolar and bipolar electrooculography signal characteristics due to target displacements-have we seen the whole picture?

The development of electrooculography (EOG)-based human-computer interface systems is generally based on the processing of the commonly referred to horizontal and vertical bipolar EOG channels, which are computed from a horizontally-aligned and another vertically-aligned pair of electrodes, respectively. Horizontal (vertical) target displacements are assumed to result in changes in the horizontal (vertical) EOG channel only, and any cross-talk between the bipolar channels is often neglected or incorrectly attributed solely to electrode misalignment with respect to the ocular rotation axes.Objective. The aim of this work is to demonstrate that such cross-talk is intrinsic to the geometric relationship between the orientation of the verging ocular globes and the planar displacement of the gaze target with respect to the primary gaze position.Approach. Since it is difficult to record actual EOG data with electrodes which are perfectly-aligned with the ocular rotation axes, this is studied by simulating the EOG potential values for various horizontally- and vertically-displacing targets using a dipole model of the eye.Main results. We show that cross-talk between the horizontal and vertical bipolar EOG channels is manifested even if the electrodes are aligned with the ocular rotation axes. Specifically, for a horizontally- (vertically-)displaced target, while the monopolar EOG signals obtained from the horizontally- (vertically-)aligned electrodes exhibit an expected predominant potential displacement, a smaller displacement is also exhibited in the monopolar EOG signals obtained from the vertically- (horizontally-)aligned electrodes. These unexpected displacements in the vertically- (horizontally-)aligned monopolar channels may have different magnitudes, resulting in an effective potential displacement in the vertical (horizontal) bipolar EOG channel.Significance. This is significant as it shows that, unlike in many works published so far for EOG-based ocular pose estimation, it is not sufficient to only use the horizontal (vertical) bipolar EOG channel to estimate the horizontal (vertical) displacement of the ocular pose.

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control.

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.

TEMoD: Target-Enabled Model-Based De-Drifting of the EOG Signal Baseline using a Battery Model of the Eye.

The electrooculography (EOG) signal baseline is subject to drifting, and several different techniques to mitigate this drift have been proposed in the literature. Some of these techniques, however, disrupt the overall ocular pose-induced DC characteristics of the EOG signal and may also require the data to be zero-centred, which means that the average point of gaze (POG) has to lie at the primary gaze position. In this work, we propose an alternative baseline drift mitigation technique which may be used to de-drift EOG data collected through protocols where the subject gazes at known targets. Specifically, it uses the target gaze angles (GAs) in a battery model of the eye to estimate the ocular pose-induced component, which is then used for baseline drift estimation. This method retains the overall signal morphology and may be applied to non-zero-centred data. The performance of the proposed baseline drift mitigation technique is compared to that of five other techniques which are commonly used in the literature, with results showing the general superior performance of the proposed technique.

SAT: A Switch-And-Train Framework for Real-Time Training of SSVEP-based BCIs.

Reducing the training time for brain computer interfaces based on steady state evoked potentials, is essential to develop practical applications. We propose to eliminate the training required by the user before using the BCI with a switch-and-train (SAT) framework. Initially the BCI uses a training-free detection algorithm, and once sufficient training data is collected online, the BCI switches to a subject-specific training-based algorithm. Furthermore, the training-based algorithm is continuously re-trained in real-time. The performance of the SAT framework reached that of training-based algorithms for 8 out of 10 subjects after an average of 179 s ±33 s, an overall improvement over the training-free algorithm of 8.06%.

EOG-Based Gaze Angle Estimation Using a Battery Model of the Eye.

In this work, a novel method to estimate the gaze angles using electrooculographic (EOG) signals is presented. Specifically, this work investigates the use of a battery model of the eye, which relates the recorded EOG potential with the distances between the corresponding electrode and the centre points of the cornea and retina, for gaze angle estimation. Using this method a cross-validated horizontal and vertical gaze angle error of 2.42±0.91° and 2.30±0.50° respectively was obtained across six subjects, demonstrating that the proposed methods and the battery model may be used to estimate the user's ocular pose reliably.

A systematic review investigating the relationship of electroencephalography and magnetoencephalography measurements with sensorimotor upper limb impairments after stroke.

BACKGROUND: Predicting sensorimotor upper limb outcome receives continued attention in stroke. Neurophysiological measures by electroencephalography (EEG) and magnetoencephalography (MEG) could increase the accuracy of predicting sensorimotor upper limb recovery. NEW METHOD: The aim of this systematic review was to summarize the current evidence for EEG/MEG-based measures to index neural activity after stroke and the relationship between abnormal neural activity and sensorimotor upper limb impairment. Relevant papers from databases EMBASE, CINHAL, MEDLINE and pubMED were identified. Methodological quality of selected studies was assessed with the Modified Downs and Black form. Data collected was reported descriptively. RESULTS: Seventeen papers were included; 13 used EEG and 4 used MEG applications. Findings showed that: (a) the presence of somatosensory evoked potentials in the acute stage are related to better outcome of upper limb motor impairment from 10 weeks to 6 months post-stroke; (b) an interhemispheric imbalance of cortical oscillatory signals associated with upper limb impairment; and (c) predictive models including beta oscillatory cortical signal factors with corticospinal integrity and clinical measures could enhance upper limb motor prognosis. COMPARING WITH EXISTING METHOD: The combination of neurological biomarkers with clinical measures results in higher statistical power than using neurological biomarkers alone when predicting motor recovery in stroke. CONCLUSIONS: Alterations in neural activity by means of EEG and MEG are demonstrated from the early post-stroke stage onwards, and related to sensorimotor upper limb impairment. Future work exploring cortical oscillatory signals in the acute stage could provide further insight about prediction of upper limb sensorimotor recovery.

Phase-based SSVEPs for real-time control of a motorised bed.

Brain-computer interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.

EEG-based biometry using steady state visual evoked potentials.

The use of brain signals for person recognition has in recent years attracted considerable interest because of the increased security and privacy these can offer when compared to conventional biometric measures. The main challenge lies in extracting features from the EEG signals that are sufficiently distinct across individuals while also being sufficiently consistent across multiple recording sessions. A range of EEG phenomena including eyes open and eyes closed activity, visual evoked potentials (VEPs) through image presentation, and other mental tasks have been studied for their use in biometry.

Semi-supervised segmentation of EEG data in BCI systems.

This work investigates the use of a semi-supervised, autoregressive switching multiple model (AR-SMM) framework for the segmentation of EEG data applied to brain computer interface (BCI) systems. This gives the possibility of identifying and learning novel modes within the data, giving insight on the changing dynamics of the EEG data and possibly also offering a solution for shorter training periods in BCIs. Furthermore it is shown that the semi-supervised model allocation process is robust to different starting positions and gives consistent results.