About
61
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Introduction
I am a research fellow in clinical neuroscience, University of Oxford. My research focuses on brain computer interface and neuromodulation.
Skills and Expertise
Current institution
Additional affiliations
February 2022 - present
May 2018 - January 2022
Education
September 2011 - December 2017
Field of study
- Pattern Recognition and Intelligent System; Brain-Computer Interface
September 2007 - July 2011
Publications
Publications (61)
Essential tremor (ET) is one of the most common movement disorders in adults. Deep brain stimulation (DBS) of the ventralis intermediate nucleus (VIM) of the thalamus and/or the posterior subthalamic area (PSA) has been shown to provide significant tremor suppression in patients with ET, but with significant inter-patient variability and habituatio...
Subthalamic nucleus (STN) beta-triggered adaptive deep brain stimulation (ADBS) has been shown to provide clinical improvement comparable to conventional continuous DBS (CDBS) with less energy delivered to the brain and less stimulation induced side-effects. However, several questions remain unanswered. First, there is a normal physiological reduct...
The pedunculopontine nucleus (PPN) is a reticular collection of neurons at the junction of the midbrain and pons, playing an important role in modulating posture and locomotion. Deep brain stimulation of the PPN has been proposed as an emerging treatment for patients with Parkinson's disease (PD) or multiple system atrophy (MSA) who have gait-relat...
Background:
High-frequency thalamic stimulation is an effective therapy for essential tremor, which mainly affects voluntary movements and/or sustained postures. However, continuous stimulation may deliver unnecessary current to the brain due to the intermittent nature of the tremor.
Objective:
We proposed to close the loop of thalamic stimulati...
Previous studies have explored neurofeedback training for Parkinsonian patients to suppress beta oscillations in the subthalamic nucleus (STN). However, its impacts on movements and Parkinsonian tremor are unclear. We developed a neurofeedback paradigm targeting STN beta bursts and investigated whether neurofeedback training could improve motor ini...
Objectives
Parkinson's disease (PD) is a neurodegenerative disorder that can cause motor impairments and gait problems. Non-invasive electrophysiological biomarkers like phase amplitude coupling (PAC) hold promise for understanding and managing PD. This study aims to investigate the modulations of levodopa and locomotion in cortical and cortico-mus...
Background
Phase-locked neuromodulation aligns electrical or magnetic stimulation with the brain’s natural rhythms, showing promising potential to enhance therapeutic outcomes by more precisely modulating specific neural oscillations. However, stimulation-induced artifacts critically compromise real-time phase estimation accuracy. Existing approach...
Background
Tremor phase-locked deep brain stimulation (DBS) has been shown to modulate symptom severity in patients with postural tremor, including essential (ET) and dystonic tremor (DT). This, provides a potential alternative therapy that targets the underlying pathological oscillations with less energy delivered to the brain than existing system...
Freezing of gait (FOG) is a devastating symptom of Parkinson's disease (PD) often resulting in disabling falls and loss of independence. It affects half of patients, yet current therapeutic strategies are insufficient, and the underlying neural mechanisms remain poorly understood. This study investigated beta oscillation dynamics in the STN during...
Objective: Understanding the neural mechanisms underlying movement initiation is crucial for advancing movement-driven adaptive deep brain stimulation therapies for tremor disorders. We investigated the feasibility of decoding pre-movement periods of upper limb movements by machine learning using thalamic local field potentials (LFPs) and scalp ele...
Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation appro...
Brain-computer interfaces (BCIs) provide a means of translating neural activity into movement for stroke rehabilitation. Electroencephalography (EEG)-based motor imagery (MI) is a cognitive strategy to enhance motor recovery after stroke. However, traditional MI-BCI systems require extensive calibration before conducting online experiments, thus co...
Background
Deep brain stimulation is a treatment for advanced Parkinson's disease and currently tuned to target motor symptoms during daytime. Parkinson's disease is associated with multiple nocturnal symptoms such as akinesia, insomnia, and sleep fragmentation, which may require adjustments of stimulation during sleep for best treatment outcome....
Essential tremor (ET) is one of the most common movement disorders in adults. Deep brain stimulation (DBS) of the ventralis intermediate nucleus (VIM) of the thalamus and/or the posterior subthalamic area (PSA) has been shown to provide significant tremor suppression in patients with ET, but with significant inter-patient variability and habituatio...
Background: Deep brain stimulation is a treatment for advanced Parkinson's disease and currently tuned to target motor symptoms during daytime. Parkinson's disease is associated with multiple nocturnal symptoms such as akinesia, insomnia and sleep fragmentation which may require adjustments of stimulation during sleep for best treatment outcome.
Ob...
How cortical oscillations are involved in the coordination of functionally coupled muscles and how this is modulated by different movement contexts (static vs dynamic) remains unclear. Here, this is investigated by recording high-density electroencephalography (EEG) and electromyography (EMG) from different forearm muscles while healthy participant...
Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used cli...
Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep...
Background
The management of patients with disorders of consciousness (DOC) presents substantial challenges in clinical practice. Deep brain stimulation (DBS) has emerged as a potential therapeutic approach, but the lack of standardized regulatory parameters for DBS in DOC hinders definitive conclusions.
Objective
This comprehensive review aims to...
Background: Everyday decision-making requires the ability to flexibly modify and sometimes terminate our actions, such as avoiding a tempting slice of cake to hitting the brakes in an emergency. Neural oscillations, such as beta-band rhythms observed over the medial prefrontal cortex(mPFC), help regulate these context-dependent behaviours. However,...
Beyond the established effects of subthalamic nucleus deep brain stimulation (STN-DBS) in reducing motor symptoms in Parkinson’s disease, recent evidence has highlighted the effect on non-motor symptoms. However, the impact of STN-DBS on disseminated networks remains unclear. This study aimed to perform a quantitative evaluation of network-specific...
In patients with Parkinson’s disease (PD), suppression of beta and increase in gamma oscillations in the subthalamic nucleus (STN) have been associated with both levodopa treatment and motor functions. Recent results suggest that modulation of the temporal dynamics of theses oscillations (bursting activity) might contain more information about path...
Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonica...
Introduction
Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled huma...
Brain computer interfaces (BCIs) have been demonstrated to have the potential to enhance motor recovery after stroke. However, some stroke patients with severe paralysis have difficulty achieving the BCI performance required for participating in BCI-based rehabilitative interventions, limiting their clinical benefits. To address this issue, we pres...
p>Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used c...
Evoked resonant neural activity (ERNA) is induced by subthalamic deep brain stimulation (DBS) and was recently suggested as a marker of lead placement and contact selection in Parkinson's disease. Yet, its underlying mechanisms and how it is modulated by stimulation parameters are unclear. Here, we recorded local field potentials from 27 Parkinson'...
Brainstem nuclei, such as the pedunculopontine nucleus, send activating projections to cortex, modulating states of sleep, wakefulness and arousal levels. Surgical modulation of subcortical activity using deep brain stimulation (DBS) is utilised in the management of pain and movement disorders. DBS of brainstem arousal circuits in a state-dependent...
Background:
Subthalamic nucleus (STN) stimulation is an effective treatment for Parkinson's disease and induced local field potential (LFP) changes that have been linked with clinical improvement. STN stimulation has also been used in dystonia although the internal globus pallidus is the standard target where theta power has been suggested as a ph...
Subthalamic nucleus (STN) beta-triggered adaptive deep brain stimulation (ADBS) has been shown to provide clinical improvement comparable to conventional continuous DBS (CDBS) in people with Parkinson's disease (PD) with less energy delivered to the brain and less stimulation induced side-effects. However, several questions remain unanswered. First...
Background
In Essential tremor (ET), involuntary shaking of the upper limbs during isometric muscle contraction closely reflects the patterns of neural activity measured in the thalamus - a key element of the tremorgenic circuit. Phase-specific deep brain stimulation (DBS) builds upon this observation while using accelerometery of the trembling lim...
Exaggerated local field potential bursts of activity at frequencies in the low beta band are a well-established phenomenon in the subthalamic nucleus of patients with Parkinson’s disease. However, such activity is only moderately correlated with motor impairment. Here we test the hypothesis that beta bursts are just one of several dynamic states in...
Backgroud
The pedunculopontine nucleus (PPN) is a reticular collection of neurons at the junction of the midbrain and pons, playing an important role in modulating posture and locomotion. Deep brain stimulation of the PPN has been proposed as an emerging treatment for patients with Parkinson’s disease (PD) or multiple system atrophy (MSA) suffering...
Finely-tuned gamma (FTG) oscillations can be recorded from cortex or the subthalamic nucleus (STN) in patients with Parkinson"s disease (PD) on dopaminergic medication, and have been associated with dyskinesias. When recorded during deep brain stimulation (DBS) on medication the FTG is entrained to half the stimulation frequency. We investigated wh...
Patients with advanced Parkinson's can be treated by deep brain stimulation (DBS) of the subthalamic nucleus (STN). This affords a unique opportunity to record from this nucleus and stimulate it in a controlled manner. Previous work has shown that activity in the STN is modulated in a rhythmic pattern when Parkinson's patients perform stepping move...
Patients with advanced Parkinson's can be treated by deep brain stimulation of the subthalamic nucleus (STN). This affords a unique opportunity to record from this nucleus and stimulate it in a controlled manner. Previous work has shown that activity in the STN is modulated in a rhythmic pattern when Parkinson's patients perform stepping movements,...
Neural oscillating patterns, or time-frequency features, predicting voluntary motor intention, can be extracted from the local field potentials (LFPs) recorded from the sub-thalamic nucleus (STN) or thalamus of human patients implanted with deep brain stimulation (DBS) electrodes for the treatment of movement disorders. This paper investigates the...
High frequency Deep Brain Stimulation (DBS) targeting the motor thalamus is an effective therapy for essential tremor (ET). However, since tremor mainly affects periods of voluntary movements and sustained postures in ET, conventional continuous stimulation may deliver unnecessary current to the brain. Here we tried to decode movement states based...
Continuous high frequency Deep Brain Stimulation (DBS) is a standard therapy for several neurological disorders. Closed-loop DBS is expected to further improve treatment by providing adaptive, on-demand therapy. Local field potentials (LFPs) recorded from the stimulation electrodes are the most often used feedback signal in closed-loop DBS. However...
Abnormally increased b bursts in cortical-basal ganglia-thalamic circuits are associated with rigidity and bradykinesia in patients with Parkinson's disease. Increased b bursts detected in the motor cortex have also been associated with longer reaction times (RTs) in healthy participants. Here we further hypothesize that suppressing b bursts throug...
Objective:
A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI...
This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrates a speller, a web browser, an e-mail client, and a file explorer using electroencephalographic (EEG) and electrooculography (EOG) signals. More specifically, an EOG-based button selection method, which requires the user to blink his/her eyes synchrono...
Most existing brain-computer Interfaces (BCIs) are designed to control a single assistive device, such as a wheelchair, a robotic arm or a prosthetic limb. However, many daily tasks require combined functions which can only be realized by integrating multiple robotic devices. Such integration raises the requirement of the control accuracy and is mo...
Enhanced beta oscillations (13-30 Hz) in the subthalamic nucleus (STN) have been associated with clinical impairment in Parkinson’s disease (PD), such as rigidity and slowing of movement, with the suppression of STN beta activity through medication or deep brain stimulation correlating with improvement in these symptoms. Recent studies have also em...
Increased oscillatory activities in the beta frequency band (13-30 Hz) in the subthalamic nucleus (STN), and in particular prolonged episodes of increased synchrony in this frequency band, have been associated with motor symptoms such as bradykinesia and rigidity in Parkinson's disease (PD). Numerous studies have investigated sensorimotor cortical...
Objective. In this study, we combine a wheelchair and an intelligent robotic arm based on an electrooculogram (EOG) signal to help patients with spinal cord injuries (SCIs) accomplish a self-drinking task. The main challenge is to accurately control the wheelchair to ensure that the randomly located object is within a limited reachable space of the...
Background:
High frequency Deep brain stimulation (DBS) targeting motor thalamus is an effective therapy for essential tremor (ET). However, conventional continuous stimulation may deliver unnecessary current to the brain since tremor mainly affects voluntary movements and sustained postures in ET.
Objective:
We aim to decode both voluntary move...
Objective:
This paper presents an asynchronous EOG-based human machine interface (HMI) for smart home environmental control with the purpose of providing daily assistance for severe spinal cord injury (SCI) patients.
Methods:
The proposed HMI allows users to interact with a smart home environment through eye blinking. Specifically, several butto...
Biological signals, including electroencephalography (EEG) and electrooculography (EOG), are often used to develop switches, which represent a class of typical asynchronous human-computer interfaces (HCIs) in which control and idle states need to be distinguished based on a criterion. Determining a satisfactory criterion for rapid and accurate disc...
Objective:
Non-manual human-machine interfaces (HMIs) have been studied for wheelchair control with the aim of helping severely paralyzed individuals regain some mobility. The challenge is to rapidly, accurately and sufficiently produce control commands, such as left and right turns, forward and backward motions, acceleration, deceleration, and st...
In this study, we propose a new web browser based on a hybrid brain computer interface (BCI) combining electroencephalographic (EEG) and electrooculography (EOG) signals. Specifically, the user can control the horizontal movement of the mouse by imagining left/right hand motion, and control the vertical movement of the mouse, select/reject a target...
Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as...
Methods:
An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving selfpaced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate. By contrast, when the user selects a function of the d...
The key issue of electroencephalography (EEG)- based brain switches is to detect the control and idle states in an asynchronous manner. Most existing methods rely on a threshold. However, it is often time consuming to select a satisfactory threshold, and the chosen threshold might be inappropriate over a long period of time due to the variability o...
P300, which is usually evoked by visual stimulus, is widely used in electroencephalography (EEG) based brain computer interface (BCI) studies. As an application-oriented BCI study, the P300 speller would inevitably be used in outdoor environments. However, the visual stimulation effect might be interfered by the reflections in outdoor environment....
Property protection is an important item of video surveillance, which is necessary to automatically distinguish missingness from interference quickly and accurately. Two simple methods are proposed in this paper. One is based on foreground feature reference using a Features from Accelerated Segment Test (FAST) corner spatial matching algorithm. The...
Questions
Question (1)
I would like to try extreme learning machine (ELM) proposed by Dr. Huang for binary classification. My features include powers calculated in different frequency bands using data extracted from different time windows before the current classification time point. Thus, in addition to the classification accuracy, I am also interested in the contributions of different features. The question is how can I quantify the feature contribution by using ELM?
Many thanks
Shenghong
