The Experts below are selected from a list of 63255 Experts worldwide ranked by ideXlab platform
Sicong Zhang - One of the best experts on this subject based on the ideXlab platform.
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highly interactive brain computer interface based on flicker free steady state motion visual Evoked Potential
Scientific Reports, 2018Co-Authors: Guanghua Xu, Chaoyang Chen, Sicong ZhangAbstract:Visual Evoked Potential-based brain–computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual Evoked Potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs Evoked “single fundamental peak” responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.
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steady state motion visual Evoked Potential ssmvep based on equal luminance colored enhancement
PLOS ONE, 2017Co-Authors: Wenqiang Yan, Jun Xie, Chengcheng Han, Sicong Zhang, Ailing Luo, Chaoyang ChenAbstract:Steady-state visual Evoked Potential (SSVEP) is one of the typical stimulation paradigms of brain-computer interface (BCI). It has become a research approach to improve the performance of human-computer interaction, because of its advantages including multiple objectives, less recording electrodes for electroencephalogram (EEG) signals, and strong anti-interference capacity. Traditional SSVEP using light flicker stimulation may cause visual fatigue with a consequent reduction of recognition accuracy. To avoid the negative impacts on the brain response caused by prolonged strong visual stimulation for SSVEP, steady-state motion visual Evoked Potential (SSMVEP) stimulation method was used in this study by an equal-luminance colored ring-shaped checkerboard paradigm. The movement patterns of the checkerboard included contraction and expansion, which produced less discomfort to subjects. Feature recognition algorithms based on power spectrum density (PSD) peak was used to identify the peak frequency on PSD in response to visual stimuli. Results demonstrated that the equal-luminance red-green stimulating paradigm within the low frequency spectrum (lower than 15 Hz) produced higher power of SSMVEP and recognition accuracy than black-white stimulating paradigm. PSD-based SSMVEP recognition accuracy was 88.15±6.56%. There was no statistical difference between canonical correlation analysis (CCA) (86.57±5.37%) and PSD on recognition accuracy. This study demonstrated that equal-luminance colored ring-shaped checkerboard visual stimulation Evoked SSMVEP with better SNR on low frequency spectrum of power density and improved the interactive performance of BCI.
Chaoyang Chen - One of the best experts on this subject based on the ideXlab platform.
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highly interactive brain computer interface based on flicker free steady state motion visual Evoked Potential
Scientific Reports, 2018Co-Authors: Guanghua Xu, Chaoyang Chen, Sicong ZhangAbstract:Visual Evoked Potential-based brain–computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual Evoked Potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs Evoked “single fundamental peak” responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.
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steady state motion visual Evoked Potential ssmvep based on equal luminance colored enhancement
PLOS ONE, 2017Co-Authors: Wenqiang Yan, Jun Xie, Chengcheng Han, Sicong Zhang, Ailing Luo, Chaoyang ChenAbstract:Steady-state visual Evoked Potential (SSVEP) is one of the typical stimulation paradigms of brain-computer interface (BCI). It has become a research approach to improve the performance of human-computer interaction, because of its advantages including multiple objectives, less recording electrodes for electroencephalogram (EEG) signals, and strong anti-interference capacity. Traditional SSVEP using light flicker stimulation may cause visual fatigue with a consequent reduction of recognition accuracy. To avoid the negative impacts on the brain response caused by prolonged strong visual stimulation for SSVEP, steady-state motion visual Evoked Potential (SSMVEP) stimulation method was used in this study by an equal-luminance colored ring-shaped checkerboard paradigm. The movement patterns of the checkerboard included contraction and expansion, which produced less discomfort to subjects. Feature recognition algorithms based on power spectrum density (PSD) peak was used to identify the peak frequency on PSD in response to visual stimuli. Results demonstrated that the equal-luminance red-green stimulating paradigm within the low frequency spectrum (lower than 15 Hz) produced higher power of SSMVEP and recognition accuracy than black-white stimulating paradigm. PSD-based SSMVEP recognition accuracy was 88.15±6.56%. There was no statistical difference between canonical correlation analysis (CCA) (86.57±5.37%) and PSD on recognition accuracy. This study demonstrated that equal-luminance colored ring-shaped checkerboard visual stimulation Evoked SSMVEP with better SNR on low frequency spectrum of power density and improved the interactive performance of BCI.
Gavin N. C. Kenny - One of the best experts on this subject based on the ideXlab platform.
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Auditory Evoked Potential index: a quantitative measure of changes in auditory Evoked Potentials during general anaesthesia
Anaesthesia, 1997Co-Authors: H. Mantzaridis, Gavin N. C. KennyAbstract:We describe a novel index derived from the auditory Evoked Potential, the auditory Evoked Potential index, and we compare it with latencies and amplitudes related to clinical signs of consciousness and unconsciousness. Eleven patients, scheduled for total knee replacement under spinal anaesthesia, completed the study. The initial mean (SD) value of the auditory Evoked Potential index was 72.5 (11.2). During the first period of unconsciousness it decreased to 39.6 (6.9) and returned to 66.8 (12.5) when patients regained consciousness. Thereafter, similar values were obtained whenever patients lost and regained consciousness. Latencies and amplitudes changed in a similar fashion. From all parameters studied, Na latencies had the greatest overlap between successive awake and asleep states. The auditory Evoked Potential index and Nb latencies had no overlap. The consistent changes demonstrated suggest that the auditory Evoked Potential index could be used as a reliable indicator of Potential awareness during propofol anaesthesia instead of latencies and amplitudes.
Dominic Nardi - One of the best experts on this subject based on the ideXlab platform.
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evaluation of intraoperative somatosensory Evoked Potential monitoring during 100 cervical operations
Spine, 1993Co-Authors: Nancy E Epstein, Joseph Danto, Dominic NardiAbstract:Continuous intraoperative somatosensory-Evoked Potential monitoring during scoliosis surgery, along with improved instrumentation techniques, has contributed to the reduction of neurologic injury from 4-6.9% to 0-07%. To assess whether somatosensory-Evoked Potential monitoring might play a similar r
Donald J Crammond - One of the best experts on this subject based on the ideXlab platform.
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abstract tp80 improving perioperative stroke detection during intracranial aneurysm surgery using somatosensory Evoked Potential waveform analysis
Stroke, 2018Co-Authors: Eyad E Saca, Ahmed Kashkoush, Donald J Crammond, Jeffrey Balzer, Parthasarathy D ThirumalaAbstract:Background: Current alarm criteria for evaluating impending perioperative stroke using somatosensory Evoked Potential (SSEP) intraoperative monitoring is a 50% reduction in amplitude or a 10% incre...
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diagnostic accuracy of combined multimodality somatosensory Evoked Potential and transcranial motor Evoked Potential intraoperative monitoring in patients with idiopathic scoliosis
Spine, 2016Co-Authors: Parthasarathy D Thirumala, Jessie Huang, Hannah Cheng, Karthy Thiagarajan, Jeffrey Balzer, Donald J CrammondAbstract:STUDY DESIGN: Systematic review. OBJECTIVE: The aim of the study was to determine the predictive value of combined multimodality somatosensory Evoked Potential (SSEP) and transcranial motor Evoked Potential (TcMEP) monitoring in detecting impending neurological injury during surgery for idiopathic scoliosis. SUMMARY OF BACKGROUND DATA: The diagnostic of motor Evoked Potential monitoring and SSEP monitoring have been established. However, the predictive value of combined multimodality SSEP and TcMEP monitoring in detecting impending neurological injury during surgery for idiopathic scoliosis has not been evaluated. METHODS: A systematic literature search was performed using PubMed/MEDLINE, Web of Science, and EMBASE from 1974 to January 2015. All titles and abstracts were independently reviewed by the authors. We included all studies that were (1) randomized controlled trials, prospective or retrospective cohort studies; (2) included patients with idiopathic scoliosis undergoing scoliosis correction surgery; (3) included multimodality SSEP and TcMEP monitoring during spinal surgery; (4) included immediate postoperative neurological assessment; (5) idiopathic scoliosis patient population n ≥25; and (6) published in English. RESULTS: Seven studies comprising a total of 2052 patients with idiopathic scoliosis were included in our meta-analysis. The incidence of neurological deficit in this cohort was 0.93%. The pooled sensitivity, specificity, and Diagnostic Odds Ratio were 82.6% (95% CI 56.7%-94.5%), 94.4% (95% CI 85.1%-98.0%), and 106.16 (95% CI 24.952-451.667), respectively. The area under the curve was 0.928, indicating excellent discriminatory ability. CONCLUSION: Idiopathic scoliosis corrective surgery patients who experience a new neurological deficit are 106.16 times more likely to have had an SSEP and/or TcMEP change during corrective procedures. The results of this meta-analysis demonstrate that combined multimodality SSEP and TcMEP monitoring possess some advantage over use of each alone, and that intraoperative neurophysiological monitoring may provide a valuable biomarker in detection of impending neurological injury. LEVEL OF EVIDENCE: 2.