scholarly journals Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yue Li ◽  
Shaomin Zhang ◽  
Yile Jin ◽  
Bangyu Cai ◽  
Marco Controzzi ◽  
...  
Keyword(s):  
Sensorimotor Cortex ◽  
Signal Source ◽  
Central Sulcus ◽  
Brain Machine Interfaces ◽  
Hand Gestures ◽  
Neural Signal ◽  
Hybrid Frequency ◽  
Offline Analysis ◽  
Online Tests ◽  
Decoding Accuracy

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures.

scholarly journals A Reconfigurable Neural Signal Processor (NSP) for Brain Machine Interfaces

2006 ◽  
Author(s):  
Shalom Darmanjian ◽  
Grzegorz Cieslewski ◽  
Scott Morrison ◽  
Benjamin Dang ◽  
Karl Gugel ◽  
...  
Keyword(s):  
Brain Machine Interfaces ◽  
Neural Signal ◽  
Signal Processor

Localization of human sensorimotor cortex during surgery by cortical surface recording of somatosensory evoked potentials

1988 ◽  
Vol 68 (1) ◽  
pp. 99-111 ◽  
Author(s):  
Charles C. Wood ◽  
Dennis D. Spencer ◽  
Truett Allison ◽  
Gregory McCarthy ◽  
Peter D. Williamson ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
General Anesthesia ◽  
Somatosensory Evoked Potentials ◽  
Sensorimotor Cortex ◽  
Cortical Surface ◽  
Central Sulcus ◽  
Content Type ◽  
Postcentral Gyrus ◽  
Hand Area ◽  
Fine Print

✓ The traditional means of localizing sensorimotor cortex during surgery is Penfield's procedure of mapping sensory and motor responses elicited by electrical stimulation of the cortical surface. This procedure can accurately localize sensorimotor cortex but is time-consuming and best carried out in awake, cooperative patients. An alternative localization procedure is presented that involves cortical surface recordings of somatosensory evoked potentials (SEP's), providing accurate and rapid localization in patients under either local or general anesthesia. The morphology and amplitude of median nerve SEP's recorded from the cortical surface varied systematically as a function of spatial location relative to the sensorimotor hand representation area. These results were validated in 18 patients operated on under local anesthesia in whom the sensorimotor cortex was independently localized by electrical stimulation mapping; the two procedures were in agreement in all cases. Similar SEP results were demonstrated in an additional 27 patients operated on under general anesthesia without electrical stimulation mapping. The following three spatial relationships between SEP's and the anatomy of the sensorimotor cortex permit rapid and accurate localization of the sensorimotor hand area: 1) SEP's with approximately mirror-image waveforms are recorded at electrode sites in the hand area on opposite sides of the central sulcus (P20–N30 precentrally (for consistency) and N20–P30 postcentrally); 2) the P25–N35 is recorded from the postcentral gyrus as well as a small region of the precentral gyrus in the immediate vicinity of the central sulcus: this waveform is largest on the postcentral gyrus about 1 cm medial to the focus of the 20- and 30-msec potentials; and 3) regardless of component identification, maximum SEP amplitudes are recorded from the hand representation area on the precentral and postcentral gyri.

scholarly journals Corrigendum to “Handedness Side and Magnetization Transfer Ratio in the Primary Sensorimotor Cortex Central Sulcus”

2020 ◽  
Vol 2020 ◽  
pp. 1-1
Author(s):  
Mario Mascalchi ◽  
Stefano Ciulli ◽  
Andrea Bianchi ◽  
Chiara Marzi ◽  
Stefano Orsolini ◽  
...  
Keyword(s):  
Sensorimotor Cortex ◽  
Magnetization Transfer ◽  
Magnetization Transfer Ratio ◽  
Central Sulcus ◽  
Transfer Ratio ◽  
Primary Sensorimotor Cortex

scholarly journals Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhengwu Liu ◽  
Jianshi Tang ◽  
Bin Gao ◽  
Peng Yao ◽  
Xinyi Li ◽  
...  
Keyword(s):  
Signal Analysis ◽  
High Efficiency ◽  
Brain Machine Interfaces ◽  
Neural Signal ◽  
Neural Signal Analysis

scholarly journals On the delay in propagation of action potentials

2019 ◽  
Author(s):  
J. Xu ◽  
S. Xu ◽  
F. Wang ◽  
S. Xu
Keyword(s):  
Experimental Data ◽  
Action Potential ◽  
Delay Time ◽  
Action Potentials ◽  
Physical Mechanism ◽  
Average Duration ◽  
Signal Source ◽  
Neural Signals ◽  
Neural Signal ◽  
Electromagnetic Soliton

AbstractThe signal delay during the propagation of action potentials is one of the key issues in understanding the mechanisms of generation and propagation of neural signals. Here we reanalyzed related experimental data to demonstrate that action potentials in the propagation process along a myelinated axon are highly overlapped in the time scale. The shift in time of two successive signals from neighboring nodes, defined as delay time τ in this work, is only tens of microseconds (16.3-87.0 μs), thus is only ~ 0.8-4.4 % of the measured average duration of an action potential, ~ 2 ms. This fact may reveal a huge gap to the commonly accepted picture for propagation of neural signal. We could apply the electromagnetic soliton-like model to well explain this phenomenon, and attribute τ to the waiting time that one signal source (i.e., ion channel cluster at one node) needs to take when it generates an electromagnetic neural pulse with increasing intensity until the intensity is higher than a certain point so as to activate neighboring signal source. This viewpoint may shed some light on a better understanding of the exact physical mechanism of neural signal communication in a variety of biosystems.Statement of SignificanceThe delay time during the propagation of action potentials is an important term in understanding the mechanisms of generation and propagation of neural signals. In this article we analyzed published experimental data and showed that action potentials from two neighboring Ranvier nodes are highly overlapped in time, with an average shift of tens of microseconds, which occupied only ~ 0.8-4.4 % of the average duration of an action potential (2 ms). The electromagnetic soliton-model seemed the best model to explain this phenomenon.The viewpoint of this article may shed some light on a better understanding of the exact physical mechanism of neural signal communication, and be tractive to researchers in a variety of fields, such as neuroscience, brain-computer interface, etc..

scholarly journals Enhancing Gesture Decoding Performance Using Signals from Posterior Parietal Cortex: A Stereo-Electroencephalograhy (SEEG) Study

2019 ◽  
Author(s):  
Meng Wang ◽  
Guangye Li ◽  
Shize Jiang ◽  
Zixuan Wei ◽  
Jie Hu ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Sensorimotor Cortex ◽  
Posterior Parietal Cortex ◽  
Hand Movement ◽  
Functional Independence ◽  
Support Vector ◽  
Visuomotor Task ◽  
Primary Sensorimotor Cortex ◽  
Posterior Parietal ◽  
Decoding Accuracy

AbstractObjectiveHand movement is a crucial function for humans’ daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI.ApproachStereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r2. According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding.Main ResultsWe find that a majority(L: >60%, R: >40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used.SignificanceThis is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.

Sign in / Sign up

Export Citation Format

Share Document