IEEE J Biomed Health Inform
October 2024
Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNNs capture spatiotemporal features at single spectral resolution. However, epileptiform EEG signals contain irregular neural oscillations of different frequencies in different brain regions.
View Article and Find Full Text PDFSheng Wu Yi Xue Gong Cheng Xue Za Zhi
August 2024
Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data.
View Article and Find Full Text PDF. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs).
View Article and Find Full Text PDFJ Integr Neurosci
February 2024
Background: Emotions are thought to be related to distinct patterns of neural oscillations, but the interactions among multi-frequency neural oscillations during different emotional states lack full exploration. Phase-amplitude coupling is a promising tool for understanding the complexity of the neurophysiological system, thereby playing a crucial role in revealing the physiological mechanisms underlying emotional electroencephalogram (EEG). However, the non-sinusoidal characteristics of EEG lead to the non-uniform distribution of phase angles, which could potentially affect the analysis of phase-amplitude coupling.
View Article and Find Full Text PDFIt is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
February 2024
Deep brain stimulation (DBS) is an effective treatment for neurologic disease and its clinical effect is highly dependent on the DBS leads localization and current stimulating state. However, standard human brain imaging modalities could not provide direct feedback on DBS currents spatial distribution and dynamic changes. Acoustoelectric brain imaging (AEBI) is an emerging neuroimaging method that can directly map current density distribution.
View Article and Find Full Text PDFSheng Wu Yi Xue Gong Cheng Xue Za Zhi
December 2023
Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2023
Much neurophysiological evidence revealed motor system is involved in temporal prediction. However, It remains unknown how temporal prediction influences motor-related neural representations. Thus, more neural evidence is needed to understand better how temporal prediction influences the motor.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2023
Error related potential (ErrP) is an effective control signal for the brain-computer interface (BCI). Current ErrP decoding methods can only distinguish right and wrong mental states. However, in real scenarios, error conditions often contain more detailed information, such as the degree of error, which would induce very similar ErrPs.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2023
As is well known, cognitive performances are highly influenced by cognitive load, so it is meaningful to find some ways to effectively reduce the cognitive load. In particular, aerobic exercise is a promising way. However, the neural evidence is still lacking in understanding how aerobic exercise minimizes cognitive load.
View Article and Find Full Text PDFBackground: Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain.
View Article and Find Full Text PDF. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs.
View Article and Find Full Text PDFBrain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.
View Article and Find Full Text PDF. Neuroimaging is one of the effective tools to understand the functional activities of the brain, but traditional non-invasive neuroimaging techniques are difficult to combine both high temporal and spatial resolution to satisfy clinical needs. Acoustoelectric brain imaging (ABI) can combine the millimeter spatial resolution advantage of focused ultrasound with the millisecond temporal resolution advantage of electroencephalogram signals.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
November 2023
Detecting movement intention is a typical use of brain-computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention.
View Article and Find Full Text PDFUnlabelled: Deep learning (DL) models have been proven to be effective in decoding motor imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success relies heavily on large amounts of training data, whereas EEG data collection is laborious and time-consuming. Recently, cross-dataset transfer learning has emerged as a promising approach to meet the data requirements of DL models.
View Article and Find Full Text PDFBased on acoustoelectric effect, acoustoelectric brain imaging has been proposed, which is a high spatiotemporal resolution neural imaging method. At the focal spot, brain electrical activity is encoded by focused ultrasound, and corresponding high-frequency acoustoelectric signal is generated. Previous studies have revealed that acoustoelectric signal can also be detected in other non-focal brain regions.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2023
Stroke often leads to permanent impairment in motor function. Accurate and quantitative prognosis of potential motor recovery before rehabilitation intervention can help healthcare centers improve resources organization and enable individualized intervention. The context of this paper investigated the potential of using electroencephalography (EEG) functional connectivity (FC) measures as biomarkers for assessing and prognosing improvement of Fugl-Meyer Assessment in upper extremity motor function ( ∆FMU) among participants with chronic stroke.
View Article and Find Full Text PDFObjective: In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement.
View Article and Find Full Text PDFCurrently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy depends deeply on the number of training samples, and the system performance would have a dramatic drop when the training dataset decreased to a small size. To date, no study has been reported to incorporate the unsupervised learning information from testing trails into the construction of supervised classification model, which is a potential way to mitigate the overfitting effect of limited samples.
View Article and Find Full Text PDFSheng Wu Yi Xue Gong Cheng Xue Za Zhi
August 2023
Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown.
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