Ren Xu

g.tec

In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the...

In brain-computer interfaces (BCIs) based on motor imagery (MI), reducing calibration time is gradually becoming an urgent issue in practical applications. Recently, transfer learning (TL) has demonstrated its effectiveness in reducing calibration time in MI-BCI. However, the different data distribution of subjects greatly affects the application e...

Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that emb...

Introduction The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the opt...

Brain-computer interfaces (BCIs) are systems that use direct real-time recordings of brain activity for communication and control. This chapter introduces the current state of the art of noninvasive and invasive BCIs. We start with a brief conceptual overview, discuss different types of input signals, and outline common control signals with some ty...

Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization a...

Objective: Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiolog...

Objective Clinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach. M...

In the motor-imagery (MI) based brain computer interface (BCI), multi-channel electroencephalogram (EEG) is often used to ensure the complete capture of physiological phenomena. With the redundant information and noise, EEG signals cannot be easily converted into separable features through feature extraction algorithms. Channel selection algorithms...

The P300-based brain–computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity fro...

The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pear...

Background Common spatial pattern (CSP) is a prevalent method applied to feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs) recorded by electroencephalogram (EEG). The selection of time windows and frequency bands prominently affects the performance of CSP algorithms. Concerning the joint optimization of these two param...

Disorders of consciousness include coma, unresponsive wakefulness syndrome (also known as vegetative state), and minimally conscious state. Neurobehavioral scales such as coma recovery scale-revised are the gold standard for disorder of consciousness assessment. Brain-computer interfaces have been emerging as an alternative tool for these patients....

Objective: Spatial and spectral features extracted from EEG are critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the common spatial pattern (CSP) and filter bank common spatial pattern (FBCSP) can effectively extract spatial-spectral features from MI-related EEG. To further improve the separability of the C...

Motor imagery (MI) based brain-computer interfaces help patients with movement disorders to regain the ability to control external devices. Common spatial pattern (CSP) is a popular algorithm for feature extraction in decoding MI tasks. However, due to noise and nonstationarity in electroencephalography (EEG), it is not optimal to combine the corre...

Task-related component analysis (TRCA) has been applied successfully in the recently popular SSVEP target recognition methods. However, a spatial filter is trained for each class in TRCA, and the training of each filter uses only the training data of the corresponding class. Therefore the information between classes is ignored in the training proce...

Introduction: Brain-computer interfaces (BCIs) provide a broad range of applications for human-computer interactions. Exploring cognitive control and underlying neurophysiological mechanisms brings essential contributions to this research field. In this paper, neurophysiological findings connected to cognitive control processes using the Stroop exp...

Objective: Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong g...

Background: Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke. Method: Studies have shown that motor imagery- (MI-) based bra...

Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms...

The evaluation of the level of consciousness in patients with disorders of consciousness (DOC) is primarily based on behavioural assessments. Patients with unresponsive wakefulness syndrome (UWS) do not show any sign of awareness of their environment, while minimally conscious state (MCS) patients show reproducible but fluctuating signs of awarenes...

Introduction:Brain-computer interfaces have become an important tool in human computer interactions. The area of applications ranges from simple research to profound stroke therapy. In this paper, a novel approach to motor imagery is proposed. We analyzed left and right hand grasping using electroencephalography (EEG) and functional near-infrared s...

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients’ cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-...

Objective: Tactile brain-computer interface (BCI) systems can provide new communication and control options for patients with impairments of eye movements or vision. One of the most common modalities used in these BCIs is the P300 potential. Until now, tactile P300 BCIs have been successfully constructed by situating tactile stimuli at various par...

Objective: Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. However, only a small number of commands (2 to 3) can be discriminated from EEG signals. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the use...

We present the feasibility of a complete rehabilitation system based on brain computer interface (BCI) triggered functional electrical stimulation (FES) and avatar mirroring to improve the function of the paretic limbs. The system was tested on two chronic stroke patients with 25 BCI training sessions over 13 weeks. The Upper-Extremity Fugl-Meyer A...

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor disablities, and thus assessing their spared cognitive abilities can be difficult. Recent research from several groups has shown that non-invasive brain-computer interface (BCI) technology can provide assessments of these patients' cognitive function that can supple...

The optimal number of EEG channels is a controversial issue for motor imagery based BCIs for stroke rehabilitation. In this study, we compared the BCI performance with 63, 27 and 16 channels of EEG on three stroke patients across 10 to 24 sessions, and demonstrated that the 16 channels montage yields similar classification error (21.3 ± 11.6, 10.5...

Brain computer interfaces (BCIs) have been employed in rehabilitation training for post-stroke patients. Patients in the chronic stage, and/or with severe paresis, are particularly challenging for conventional rehabilitation. We present results from two such patients who participated in BCI training with first-person avatar feedback. Five assessmen...

The detection of voluntary motor intention from EEG has been applied to closed-loop brain-computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study, we aim at detecting MRCPs from single-trial EEG traces. F...

In this study, we present a novel multi-class brain-computer interface (BCI) system for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. T...

Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movem...

In 2013–2014 we have advanced our MRCP-based BCI by demonstrating: (1) the ability to detect movement intent during dynamic tasks; (2) better detection accuracy than conventional approaches by implementing the locality preserving projection (LPP) approach; (3) the ability to use a single channel for accurate detection; and (4) enhanced neuroplastic...

This study aimed to improve the detection performance of movement related cortical potentials (MRCP) in EEG for brain-computer interface (BCI) applications. For this purpose, we apply an outlier-resisting manifold learning method to reduce false detection.

Brain-Computer Interfacing is a promising approach to aid the rehabilitation process of patients suffering the consequences of neurological injuries. It has been shown in recent literature that a closed-loop setup utilizing the detection of movement-related cortical potentials (MRCP) to generate afferent feedback can efficiently help the stroke pat...

For the past decade, our group worked towards the development of a non-invasive BCI system for neuromodulation. Until recently, BCIs have been used mainly for communication and replacement or restoration of lost functions for severely disabled people. Using a BCI for neuromodulation requires that the protocol closely matches the steps involved in t...

Detection of Movement-Related Cortical Potentials for BCI Applications

Non-invasive EEG-based Brain-Computer Interfaces (BCI) can be promising for the motor neuro-rehabilitation of paraplegic patients. However, this shall require detailed knowledge of the abnormalities in the EEG signatures of paraplegic patients. The association of abnormalities in different subgroups of patients and their relation to the sensorimoto...

We sought to investigate the capacity of cerebral autoregulation and cerebrovascular reactivity (CVR) in patients with middle cerebral artery (MCA) stenosis. Twenty-one patients with MCA stenosis diagnosed by magnetic resonance angiography and 15 healthy controls were enrolled. Cerebral autoregulation was assessed by autoregulatory parameters (rate...

This paper describes a novel brain-computer interface (BCI) with the aim of motor rehabilitation of stroke patients. Movement imagination of dorsiflexion was detected from scalp electroencephalogram (EEG) through movement related cortical potentials (MRCP). Such detection subsequently triggered an motorized ankle foot orthosis (MAFO), which induce...

Cerebral autoregulation is a mechanism that blood flow keeps constantly steady in spite of blood pressure variability in the brain. This mechanism has been modeled as a control system with ABP as input and CBFV as output. Linear methods of assessing CA suffer from non-linearity and non-stationarity, while newly developed methods, wavelet and MMPF,...

It is well documented that muscle stimulation may improve regional circulation. However, if this method can enhance systematic circulation remains unclear. In this pilot study, we designed an ECG-driven sequential muscle stimulation (ESMS) system and investigated if it is potentially capable of augmenting diastolic blood pressure as an alternative...