Recognition of Drivers’ Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces

Ju, Jiawei; Feleke, Aberham Genetu; Luo, Longxi; Fan, Xin-an

doi:10.34133/2022/9847652

Cited by 27 publications

(15 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brain-computer interface (BCI) can translate brain activities into commands to control external devices (Halder et al 2011, Tangwiriyasakul et al 2013, Vidaurre et al 2021. Electroencephalogram (EEG) has become the widely used a signal for BCI because of its low cost and high temporal resolution (Padfield et al 2019, Ju et al 2022. Motor imagery (MI) is a mental simulation of a specific action without any corresponding motor output.…”

Section: Introductionmentioning

confidence: 99%

A study of action difference on motor imagery based on delayed matching posture task

Zuo

Zhou

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Motor imagery (MI)-based brain-computer interfaces (BCI) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively. Approach. To explore action differences, this study proposes a novel paradigm named as Action Observation based Delayed Matching Posture Task (AO-DMPT). Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference. Main results. Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p<0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28μV of grasping, 5.64μV and 5.25μV higher than the cutting and the writing, respectively (p<0.05). Significance. The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further

show abstract

Section: Introductionmentioning

confidence: 99%

A study of action difference on motor imagery based on delayed matching posture task

Zuo

Zhou

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…LDA is the most popular algorithm for P300-based BCIs because of its low computing resource requirements and simple implementation [28,29]. LDA is a supervised dimensionality reduction method designed to increase between-class variance while reducing within-class variance.…”

Section: Various Version Of Ldamentioning

confidence: 99%

A high-speed hybrid brain-computer interface with more than 200 targets

Han¹,

Xu²,

Xiao³

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets. Approach. This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography (EEG) features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments. Main results. The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37%±7.49% and 86.00%±5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83±39.20 bits/min and 204.47±37.56 bits/min, respectively. Notably, the peak ITR could reach up to 367.83 bits/min. Significance. This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.

show abstract

“…Brain–computer interfaces (BCIs) are systems that directly measure brain activities and convert them into artificial outputs. BCIs can replace, restore, enhance, supplement, or improve the natural central nervous system outputs ( Birbaumer et al, 2008 ; Wolpaw and Wolpaw, 2012 ; Chaudhary et al, 2016 ; Coogan and He, 2018 ; Xu et al, 2021 ; Ju et al, 2022 ). Currently, scalp electroencephalogram (EEG) is the most popular brain signal for BCIs due to its relatively high temporal resolution and low cost ( Park et al, 2012 ; Xu et al, 2018 , 2020 ; Meng et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Transformed common spatial pattern for motor imagery-based brain-computer interfaces

Wang²,

Xu³

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

ObjectiveThe motor imagery (MI)-based brain–computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP.ApproachThis study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method.Main resultsAs a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively.SignificanceThe results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs.

show abstract

Recognition of Drivers’ Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces

Cited by 27 publications

References 33 publications

A study of action difference on motor imagery based on delayed matching posture task

A study of action difference on motor imagery based on delayed matching posture task

A high-speed hybrid brain-computer interface with more than 200 targets

Transformed common spatial pattern for motor imagery-based brain-computer interfaces

Contact Info

Product

Resources

About