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13 pages, 1156 KiB

Open AccessArticle

A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

by Md Rakibul Mowla, Jesus D. Gonzalez-Morales, Jacob Rico-Martinez, Daniel A. Ulichnie and David E. Thompson

Brain Sci. 2020, 10(10), 734; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10100734 - 14 Oct 2020

Cited by 6 | Viewed by 2176

Abstract

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant [...] Read more.

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (

p < 0.001

) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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16 pages, 906 KiB

Open AccessArticle

Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain–Computer Interfaces

by Piotr Stawicki and Ivan Volosyak

Brain Sci. 2020, 10(10), 686; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10100686 - 28 Sep 2020

Cited by 10 | Viewed by 2303

Abstract

Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials—SSMVEP), without employing the typical flickering. The tested [...] Read more.

Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials—SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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50 pages, 10868 KiB

Open AccessArticle

Deep Cerebellar Transcranial Direct Current Stimulation of the Dentate Nucleus to Facilitate Standing Balance in Chronic Stroke Survivors—A Pilot Study

by Zeynab Rezaee, Surbhi Kaura, Dhaval Solanki, Adyasha Dash, M V Padma Srivastava, Uttama Lahiri and Anirban Dutta

Brain Sci. 2020, 10(2), 94; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci10020094 - 10 Feb 2020

Cited by 22 | Viewed by 6117

Abstract

Objective: Cerebrovascular accidents are the second leading cause of death and the third leading cause of disability worldwide. We hypothesized that cerebellar transcranial direct current stimulation (ctDCS) of the dentate nuclei and the lower-limb representations in the cerebellum can improve functional reach during [...] Read more.

Objective: Cerebrovascular accidents are the second leading cause of death and the third leading cause of disability worldwide. We hypothesized that cerebellar transcranial direct current stimulation (ctDCS) of the dentate nuclei and the lower-limb representations in the cerebellum can improve functional reach during standing balance in chronic (>6 months’ post-stroke) stroke survivors. Materials and Methods: Magnetic resonance imaging (MRI) based subject-specific electric field was computed across a convenience sample of 10 male chronic (>6 months) stroke survivors and one healthy MRI template to find an optimal bipolar bilateral ctDCS montage to target dentate nuclei and lower-limb representations (lobules VII–IX). Then, in a repeated-measure crossover study on a subset of 5 stroke survivors, we compared 15 min of 2 mA ctDCS based on the effects on successful functional reach (%) during standing balance task. Three-way ANOVA investigated the factors of interest– brain regions, montages, stroke participants, and their interactions. Results: “One-size-fits-all” bipolar ctDCS montage for the clinical study was found to be PO9h–PO10h for dentate nuclei and Exx7–Exx8 for lobules VII–IX with the contralesional anode. PO9h–PO10h ctDCS performed significantly (alpha = 0.05) better in facilitating successful functional reach (%) when compared to Exx7–Exx8 ctDCS. Furthermore, a linear relationship between successful functional reach (%) and electric field strength was found where PO9h–PO10h montage resulted in a significantly (alpha = 0.05) higher electric field strength when compared to Exx7–Exx8 montage for the same 2 mA current. Conclusion: We presented a rational neuroimaging based approach to optimize deep ctDCS of the dentate nuclei and lower limb representations in the cerebellum for post-stroke balance rehabilitation. However, this promising pilot study was limited by “one-size-fits-all” bipolar ctDCS montage as well as a small sample size. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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18 pages, 2281 KiB

Open AccessArticle

Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

by Ji-Hoon Jeong, Baek-Woon Yu, Dae-Hyeok Lee and Seong-Whan Lee

Brain Sci. 2019, 9(12), 348; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9120348 - 29 Nov 2019

Cited by 42 | Viewed by 5080

Abstract

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are [...] Read more.

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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13 pages, 1353 KiB

Open AccessArticle

Distinct Montages of Slow Oscillatory Transcranial Direct Current Stimulation (so-tDCS) Constitute Different Mechanisms during Quiet Wakefulness

by Ping Koo-Poeggel, Verena Böttger and Lisa Marshall

Brain Sci. 2019, 9(11), 324; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9110324 - 14 Nov 2019

Cited by 2 | Viewed by 3460

Abstract

Slow oscillatory- (so-) tDCS has been applied in many sleep studies aimed to modulate brain rhythms of slow wave sleep and memory consolidation. Yet, so-tDCS may also modify coupled oscillatory networks. Efficacy of weak electric brain stimulation is however variable and dependent upon [...] Read more.

Slow oscillatory- (so-) tDCS has been applied in many sleep studies aimed to modulate brain rhythms of slow wave sleep and memory consolidation. Yet, so-tDCS may also modify coupled oscillatory networks. Efficacy of weak electric brain stimulation is however variable and dependent upon the brain state at the time of stimulation (subject and/or task-related) as well as on stimulation parameters (e.g., electrode placement and applied current. Anodal so-tDCS was applied during wakefulness with eyes-closed to examine efficacy when deviating from the dominant brain rhythm. Additionally, montages of different electrodes size and applied current strength were used. During a period of quiet wakefulness bilateral frontolateral stimulation (F3, F4; return electrodes at ipsilateral mastoids) was applied to two groups: ‘Group small’ (n = 16, f:8; small electrodes: 0.50 cm²; maximal current per electrode pair: 0.26 mA) and ‘Group Large’ (n = 16, f:8; 35 cm²; 0.35 mA). Anodal so-tDCS (0.75 Hz) was applied in five blocks of 5 min epochs with 1 min stimulation-free epochs between the blocks. A finger sequence tapping task (FSTT) was used to induce comparable cortical activity across sessions and subject groups. So-tDCS resulted in a suppression of alpha power over the parietal cortex. Interestingly, in Group Small alpha suppression occurred over the standard band (8–12 Hz), whereas for Group Large power of individual alpha frequency was suppressed. Group Small also revealed a decrease in FSTT performance at retest after stimulation. It is essential to include concordant measures of behavioral and brain activity to help understand variability and poor reproducibility in oscillatory-tDCS studies. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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14 pages, 1291 KiB

Open AccessArticle

Increased Voluntary Activation of the Elbow Flexors Following a Single Session of Spinal Manipulation in a Subclinical Neck Pain Population

by Mat Kingett, Kelly Holt, Imran Khan Niazi, Rasmus Wiberg Nedergaard, Michael Lee and Heidi Haavik

Brain Sci. 2019, 9(6), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9060136 - 12 Jun 2019

Cited by 8 | Viewed by 5048

Abstract

To investigate the effects of a single session of spinal manipulation (SM) on voluntary activation of the elbow flexors in participants with subclinical neck pain using an interpolated twitch technique with transcranial magnetic stimulation (TMS), eighteen volunteers with subclinical neck pain participated in [...] Read more.

To investigate the effects of a single session of spinal manipulation (SM) on voluntary activation of the elbow flexors in participants with subclinical neck pain using an interpolated twitch technique with transcranial magnetic stimulation (TMS), eighteen volunteers with subclinical neck pain participated in this randomized crossover trial. TMS was delivered during elbow flexion contractions at 50%, 75% and 100% of maximum voluntary contraction (MVC) before and after SM or control intervention. The amplitude of the superimposed twitches evoked during voluntary contractions was recorded and voluntary activation was calculated using a regression analysis. Dependent variables were analyzed with two-way (intervention × time) repeated measures ANOVAs. Significant intervention effects for SM compared to passive movement control were observed for elbow flexion MVC (p = 0.04), the amplitude of superimposed twitch (p = 0.04), and voluntary activation of elbow flexors (p =0.03). Significant within-group post-intervention changes were observed for the superimposed twitch (mean group decrease of 20.9%, p < 0.01) and voluntary activation (mean group increase of 3.0%, p < 0.01) following SM. No other significant within-group changes were observed. Voluntary activation of the elbow flexors increased immediately after one session of spinal manipulation in participants with subclinical neck pain. A decrease in the amplitude of superimposed twitch during elbow flexion MVC following spinal manipulation suggests a facilitation of motor cortical output. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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13 pages, 1576 KiB

Open AccessArticle

Self-Paced Online vs. Cue-Based Offline Brain–Computer Interfaces for Inducing Neural Plasticity

by Mads Jochumsen, Muhammad Samran Navid, Rasmus Wiberg Nedergaard, Nada Signal, Usman Rashid, Ali Hassan, Heidi Haavik, Denise Taylor and Imran Khan Niazi

Brain Sci. 2019, 9(6), 127; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9060127 - 01 Jun 2019

Cited by 15 | Viewed by 5060

Abstract

Brain–computer interfaces (BCIs), operated in a cue-based (offline) or self-paced (online) mode, can be used for inducing cortical plasticity for stroke rehabilitation by the pairing of movement-related brain activity with peripheral electrical stimulation. The aim of this study was to compare the difference [...] Read more.

Brain–computer interfaces (BCIs), operated in a cue-based (offline) or self-paced (online) mode, can be used for inducing cortical plasticity for stroke rehabilitation by the pairing of movement-related brain activity with peripheral electrical stimulation. The aim of this study was to compare the difference in cortical plasticity induced by the two BCI modes. Fifteen healthy participants participated in two experimental sessions: cue-based BCI and self-paced BCI. In both sessions, imagined dorsiflexions were extracted from continuous electroencephalogram (EEG) and paired 50 times with the electrical stimulation of the common peroneal nerve. Before, immediately after, and 30 min after each intervention, the cortical excitability was measured through the motor-evoked potentials (MEPs) of tibialis anterior elicited through transcranial magnetic stimulation. Linear mixed regression models showed that the MEP amplitudes increased significantly (p < 0.05) from pre- to post- and 30-min post-intervention in terms of both the absolute and relative units, regardless of the intervention type. Compared to pre-interventions, the absolute MEP size increased by 79% in post- and 68% in 30-min post-intervention in the self-paced mode (with a true positive rate of ~75%), and by 37% in post- and 55% in 30-min post-intervention in the cue-based mode. The two modes were significantly different (p = 0.03) at post-intervention (relative units) but were similar at both post timepoints (absolute units). These findings suggest that immediate changes in cortical excitability may have implications for stroke rehabilitation, where it could be used as a priming protocol in conjunction with another intervention; however, the findings need to be validated in studies involving stroke patients. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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9 pages, 1601 KiB

Open AccessCase Report

Wireless Computer-Supported Cooperative Work: A Pilot Experiment on Art and Brain–Computer Interfaces

by Gabriel G. De la Torre, Sara Gonzalez-Torre, Carlos Muñoz and Manuel A. Garcia

Brain Sci. 2019, 9(4), 94; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci9040094 - 25 Apr 2019

Cited by 1 | Viewed by 3717

Abstract

The present case study looked into the feasibility of using brain–computer interface (BCI) technology combined with computer-supported cooperative work (CSCW) in a wireless network. We had two objectives; first, to test the wireless BCI-based configuration and the practical use of this idea we [...] Read more.

The present case study looked into the feasibility of using brain–computer interface (BCI) technology combined with computer-supported cooperative work (CSCW) in a wireless network. We had two objectives; first, to test the wireless BCI-based configuration and the practical use of this idea we assessed workload perception in participants located several kilometers apart taking part in the same drawing task. Second, we studied the cortical activation patterns of participants performing the drawing task with and without the BCI technology. Results showed higher mental workload perception and broader cortical activation (frontal-temporal-occipital) under BCI experimental conditions. This idea shows a possible application of BCI research in the social field, where two or more users could engage in a computer networking task using BCI technology over the internet. New research avenues for CSCW are discussed and possibilities for future research are given. Full article

(This article belongs to the Special Issue Collection on Neural Engineering)

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