1
|
Liufu M, Leveroni ZM, Shridhar S, Zhou N, Yu JY. Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neuromodulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.24.609522. [PMID: 39253473 PMCID: PMC11383035 DOI: 10.1101/2024.08.24.609522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.
Collapse
|
2
|
Chang YC, Chao PH, Kuan YM, Huang CJ, Chen LF, Mao WC, Su TP, Chen SH, Wei CS. Delay Analysis in Closed-Loop EEG Phase-Triggered Transcranial Magnetic Stimulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083335 DOI: 10.1109/embc40787.2023.10340744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The recent development of closed-loop EEG phase-triggered transcranial magnetic stimulation (TMS) has advanced potential applications of adaptive neuromodulation based on the current brain state. Closed-loop TMS involves instantaneous acquisition of the EEG rhythm, timing prediction of the target phase, and triggering of TMS. However, the accuracy of EEG phase prediction algorithms is largely influenced by the system's transport delay, and their relationship is rarely considered in related work. This paper proposes a delay analysis that considers the delay of the closed-loop EEG phase-triggered TMS system as a primary factor in the validation of phase prediction algorithms. An in-silico validation using real EEG data was performed to compare the performance of commonly used algorithms. The experimental results indicate a significant influence of the total delay on the algorithm performance, and the performance ranking among algorithms varies at different levels of delay. We conclude that the delay analysis framework should be widely adopted in the design and validation of phase prediction algorithms for closed-loop EEG phase-triggered TMS systems.
Collapse
|
3
|
Pantazatos SP, Mclntosh JR, Saber GT, Sun X, Doose J, Faller J, Lin Y, Teves JB, Blankenship A, Huffman S, Goldman RI, George MS, Sajda P, Brown TR. The timing of transcranial magnetic stimulation relative to the phase of prefrontal alpha EEG modulates downstream target engagement. Brain Stimul 2023; 16:830-839. [PMID: 37187457 DOI: 10.1016/j.brs.2023.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 04/26/2023] [Accepted: 05/08/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The communication through coherence model posits that brain rhythms are synchronized across different frequency bands and that effective connectivity strength between interacting regions depends on their phase relation. Evidence to support the model comes mostly from electrophysiological recordings in animals while evidence from human data is limited. METHODS Here, an fMRI-EEG-TMS (fET) instrument capable of acquiring simultaneous fMRI and EEG during noninvasive single pulse TMS applied to dorsolateral prefrontal cortex (DLPFC) was used to test whether prefrontal EEG alpha phase moderates TMS-evoked top-down influences on subgenual, rostral and dorsal anterior cingulate cortex (ACC). Six runs (276 total trials) were acquired in each participant. Phase at each TMS pulse was determined post-hoc using single-trial sorting. Results were examined in two independent datasets: healthy volunteers (HV) (n = 11) and patients with major depressive disorder (MDD) (n = 17) collected as part of an ongoing clinical trial. RESULTS In both groups, TMS-evoked functional connectivity between DLPFC and subgenual ACC (sgACC) depended on the EEG alpha phase. TMS-evoked DLPFC to sgACC fMRI-derived effective connectivity (EC) was modulated by EEG alpha phase in healthy volunteers, but not in the MDD patients. Top-down EC was inhibitory for TMS pulses during the upward slope of the alpha wave relative to TMS timed to the downward slope of the alpha wave. Prefrontal EEG alpha phase dependent effects on TMS-evoked fMRI BOLD activation of the rostral anterior cingulate cortex were detected in the MDD patient group, but not in the healthy volunteer group. DISCUSSION Results demonstrate that TMS-evoked top-down influences vary as a function of the prefrontal alpha rhythm, and suggest potential clinical applications whereby TMS is synchronized to the brain's internal rhythms in order to more efficiently engage deep therapeutic targets.
Collapse
Affiliation(s)
- Spiro P Pantazatos
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - James R Mclntosh
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Orthopedic Surgery, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Golbarg T Saber
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Neurology, University of Chicago, Chicago, IL, 60637, USA
| | - Xiaoxiao Sun
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Jayce Doose
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Josef Faller
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Yida Lin
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Joshua B Teves
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Aidan Blankenship
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Sarah Huffman
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Robin I Goldman
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Mark S George
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, 29401, USA
| | - Paul Sajda
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA; Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA; Data Science Institute, Columbia University, New York, NY, 10027, USA.
| | - Truman R Brown
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Computer Science, Columbia University, New York, NY, 10027, USA.
| |
Collapse
|
4
|
Bigoni C, Cadic-Melchior A, Morishita T, Hummel FC. Optimization of phase prediction for brain-state dependent stimulation: a grid-search approach. J Neural Eng 2023; 20. [PMID: 36626830 DOI: 10.1088/1741-2552/acb1d8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/10/2023] [Indexed: 01/11/2023]
Abstract
Objective.Sources of heterogeneity in non-invasive brain stimulation literature can be numerous, with underlying brain states and protocol differences at the top of the list. Yet, incoherent results from brain-state-dependent stimulation experiments suggest that there are further factors adding to the variance. Hypothesizing that different signal processing pipelines might be partly responsible for heterogeneity; we investigated their effects on brain-state forecasting approaches.Approach.A grid-search was used to determine the fastest and most-accurate combination of preprocessing parameters and phase-forecasting algorithms. The grid-search was applied on a synthetic dataset and validated on electroencephalographic (EEG) data from a healthy (n= 18) and stroke (n= 31) cohort.Main results.Differences in processing pipelines led to different results; the grid-search chosen pipelines significantly increased the accuracy of published forecasting methods. The accuracy achieved in healthy was comparably high in stroke patients.Significance.This systematic offline analysis highlights the importance of the specific EEG processing and forecasting pipelines used for online state-dependent setups where precision in phase prediction is critical. Moreover, successful results in the stroke cohort pave the way to test state-dependent interventional treatment approaches.
Collapse
Affiliation(s)
- Claudia Bigoni
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland.,Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Andéol Cadic-Melchior
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland.,Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Takuya Morishita
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland.,Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland.,Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951 Sion, Switzerland.,Clinical Neuroscience, University of Geneva Medical School, 1202 Geneva, Switzerland
| |
Collapse
|
5
|
Faller J, Doose J, Sun X, Mclntosh JR, Saber GT, Lin Y, Teves JB, Blankenship A, Huffman S, Goldman RI, George MS, Brown TR, Sajda P. Daily prefrontal closed-loop repetitive transcranial magnetic stimulation (rTMS) produces progressive EEG quasi-alpha phase entrainment in depressed adults. Brain Stimul 2022; 15:458-471. [PMID: 35231608 PMCID: PMC8979612 DOI: 10.1016/j.brs.2022.02.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/31/2022] [Accepted: 02/17/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation modality that can treat depression, obsessive-compulsive disorder, or help smoking cessation. Research suggests that timing the delivery of TMS relative to an endogenous brain state may affect efficacy and short-term brain dynamics. OBJECTIVE To investigate whether, for a multi-week daily treatment of repetitive TMS (rTMS), there is an effect on brain dynamics that depends on the timing of the TMS relative to individuals' prefrontal EEG quasi-alpha rhythm (between 6 and 13 Hz). METHOD We developed a novel closed-loop system that delivers personalized EEG-triggered rTMS to patients undergoing treatment for major depressive disorder. In a double blind study, patients received daily treatments of rTMS over a period of six weeks and were randomly assigned to either a synchronized or unsynchronized treatment group, where synchronization of rTMS was to their prefrontal EEG quasi-alpha rhythm. RESULTS When rTMS is applied over the dorsal lateral prefrontal cortex (DLPFC) and synchronized to the patient's prefrontal quasi-alpha rhythm, patients develop strong phase entrainment over a period of weeks, both over the stimulation site as well as in a subset of areas distal to the stimulation site. In addition, at the end of the course of treatment, this group's entrainment phase shifts to be closer to the phase that optimally engages the distal target, namely the anterior cingulate cortex (ACC). These entrainment effects are not observed in the group that is given rTMS without initial EEG synchronization of each TMS train. CONCLUSIONS The entrainment effects build over the course of days/weeks, suggesting that these effects engage neuroplastic changes which may have clinical consequences in depression or other diseases.
Collapse
Affiliation(s)
- Josef Faller
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Jayce Doose
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Xiaoxiao Sun
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, 20115, USA
| | - James R Mclntosh
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Golbarg T Saber
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Neurology, University of Chicago, Chicago, IL, 60637, USA
| | - Yida Lin
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Joshua B Teves
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Aidan Blankenship
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Sarah Huffman
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Robin I Goldman
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Mark S George
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, 29401, USA
| | - Truman R Brown
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Paul Sajda
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA; Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA; Data Science Institute, Columbia University, New York, NY, 10027, USA.
| |
Collapse
|
6
|
A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia. ELECTRONICS 2021. [DOI: 10.3390/electronics10233037] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.
Collapse
|
7
|
Cardone P, Van Egroo M, Chylinski D, Narbutas J, Gaggioni G, Vandewalle G. Increased cortical excitability but stable effective connectivity index during attentional lapses. Sleep 2021; 44:6046202. [PMID: 33367909 DOI: 10.1093/sleep/zsaa284] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 11/24/2020] [Indexed: 11/14/2022] Open
Abstract
Modern lifestyle curtails sleep and increases nighttime work and leisure activities. This has a deleterious impact on vigilance and attention, exacerbating chances of committing attentional lapses, with potential dramatic outcomes. Here, we investigated the brain signature of attentional lapses and assessed whether cortical excitability and brain response propagation were modified during lapses and whether these modifications changed with aging. We compared electroencephalogram (EEG) responses to transcranial magnetic stimulation (TMS) during lapse and no-lapse periods while performing a continuous attentional/vigilance task at night, after usual bedtime. Data were collected in healthy younger (N = 12; 18-30 years) and older individuals (N = 12; 50-70 years) of both sexes. The amplitude and slope of the first component of the TMS-evoked potential were larger during lapses. In contrast, TMS response scattering over the cortical surface, as well as EEG response complexity, did not significantly vary between lapse and no-lapse periods. Importantly, despite qualitative differences, age did not significantly affect any of the TMS-EEG measures. These results demonstrate that attentional lapses are associated with a transient increase of cortical excitability. This initial change is not associated with detectable changes in subsequent effective connectivity-as indexed by response propagation-and are not markedly different between younger and older adults. These findings could contribute to develop models aimed to predicting and preventing lapses in real-life situations.
Collapse
Affiliation(s)
- Paolo Cardone
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Maxime Van Egroo
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Daphne Chylinski
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Justinas Narbutas
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,PsyNCog, University of Liège, Liège, Belgium
| | - Giulia Gaggioni
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Gilles Vandewalle
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| |
Collapse
|
8
|
Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model. J Pers Med 2021; 11:jpm11010038. [PMID: 33440652 PMCID: PMC7828009 DOI: 10.3390/jpm11010038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 12/29/2022] Open
Abstract
It is a technically challenging problem to assess the instantaneous brain state using electroencephalography (EEG) in a real-time closed-loop setup because the prediction of future signals is required to define the current state, such as the instantaneous phase and amplitude. To accomplish this in real-time, a conventional Yule–Walker (YW)-based autoregressive (AR) model has been used. However, the brain state-dependent real-time implementation of a closed-loop system employing an adaptive method has not yet been explored. Our primary purpose was to investigate whether time-series forward prediction using an adaptive least mean square (LMS)-based AR model would be implementable in a real-time closed-loop system or not. EEG state-dependent triggers synchronized with the EEG peaks and troughs of alpha oscillations in both an open-eyes resting state and a visual task. For the resting and visual conditions, statistical results showed that the proposed method succeeded in giving triggers at a specific phase of EEG oscillations for all participants. These individual results showed that the LMS-based AR model was successfully implemented in a real-time closed-loop system targeting specific phases of alpha oscillations and can be used as an adaptive alternative to the conventional and machine-learning approaches with a low computational load.
Collapse
|
9
|
Ghiasvand S, Dussourd CR, Liu J, Song Y, Berdichevsky Y. Variability of seizure-like activity in an in vitro model of epilepsy depends on the electrical recording method. Heliyon 2020; 6:e05587. [PMID: 33299935 PMCID: PMC7702014 DOI: 10.1016/j.heliyon.2020.e05587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/12/2020] [Accepted: 11/19/2020] [Indexed: 11/30/2022] Open
Abstract
Background Hippocampal and cortical slice-based models are widely used to study seizures and epilepsy. Seizure detection and quantification are essential components for studying mechanisms of epilepsy and assessing therapeutic interventions. To obtain meaningful signals and maximize experimental throughput, variability should be minimized. Some electrical recording methods require insertion of an electrode into neuronal tissue, change in slice chemical microenvironment, and transients in temperature and pH. These perturbations can cause acute and long-term alterations of the neuronal network which may be reflected in the variability of the recorded signal. New method In this study we investigated the effect of experimental perturbations in three local field potential (LFP) recording methods including substrate micro-wires (s-MWs), multiple electrode arrays (MEAs), and inserted micro wire electrodes (i-MW). These methods enabled us to isolate effects of different perturbations. We used organotypic hippocampal slices (OHCs) as an in-vitro model of posttraumatic epilepsy. To investigate the effect of the disturbances caused by the recording method on the paroxysmal events, we introduced jitter analysis, which is sensitive to small differences in the seizure spike timing. Results Medium replacement can introduce long-lasting perturbations. Electrode insertion increased variability on a shorter time scale. OHCs also underwent spontaneous state transitions characterized by transient increases in variability. Comparison with existing methods This new method of seizure waveform analysis allows for more sensitive assessment of variability of ictal events than simply measuring seizure frequency and duration. Conclusion We demonstrated that some of the variability in OHC recordings are due to experimental perturbations while some are spontaneous and independent of recording method.
Collapse
Affiliation(s)
| | | | - Jing Liu
- Electrical Engineering Lehigh University, United States
| | - Yu Song
- Bioengineering Lehigh University, United States
| | - Yevgeny Berdichevsky
- Bioengineering Lehigh University, United States.,Electrical Engineering Lehigh University, United States
| |
Collapse
|
10
|
Belinskaya A, Smetanin N, Lebedev MA, Ossadtchi A. Short-delay neurofeedback facilitates training of the parietal alpha rhythm. J Neural Eng 2020; 17. [PMID: 33166941 DOI: 10.1088/1741-2552/abc8d7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/09/2020] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Feedback latency was shown to be a critical parameter in a range of applications that imply learning. The therapeutic effects of neurofeedback (NFB) remain controversial. We hypothesized that often encountered unreliable results of NFB intervention could be associated with large feedback latency values that are often uncontrolled and may preclude the efficient learning. APPROACH We engaged our subjects into a parietal alpha power unpregulating paradigm faciliated by visual neurofeedback based on the invidually extracted envelope of the alpha-rhythm at P4 electrode. NFB was displayed either as soon as EEG envelope was processed, or with an extra 250 or 500-ms delay. The feedback training consisted of 15 two-minute long blocks interleaved with 15s pauses. We have also recorded two minute long baselines immediately before and after the training. MAIN RESULTS The time course of NFB-induced changes in the alpha rhythm power clearly depended on NFB latency, as shown with the adaptive Neyman test. NFB had a strong effect on the alpha-spindle incidence rate, but not on their duration or amplitude. The sustained changes in alpha activity measured after the completion of NFB training were negatively correlated to latency, with the maximum change for the shortest tested latency and no change for the longest. SIGNIFICANCE Here we for the first time show that visual NFB of parietal electroencephalographic (EEG) alpha-activity is efficient only when delivered to human subjects at short latency, which guarantees that NFB arrives when an alpha spindle is still ongoing. Such a considerable effect of NFB latency on the alpha-activity temporal structure could explain some of the previous inconsistent results, where latency was neither controlled nor documented. Clinical practitioners and manufacturers of NFB equipment should add latency to their specifications while enabling latency monitoring and supporting short-latency operations.
Collapse
Affiliation(s)
- Anastasia Belinskaya
- Centre for Bioelectric Interfaces, National Research University Higher School of Economics, Moskva, Moskva, RUSSIAN FEDERATION
| | - Nikolai Smetanin
- Centre for Bioelectric Interfaces, National Research University Higher School of Economics, Moskva, Moskva, RUSSIAN FEDERATION
| | - M A Lebedev
- Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moskva, Moskva, RUSSIAN FEDERATION
| | - Alexei Ossadtchi
- Center for bioelectirc interfaces, National Research University Higher School of Economics, Moskva, Moskva, RUSSIAN FEDERATION
| |
Collapse
|
11
|
Shirinpour S, Alekseichuk I, Mantell K, Opitz A. Experimental evaluation of methods for real-time EEG phase-specific transcranial magnetic stimulation. J Neural Eng 2020; 17:046002. [PMID: 32554882 DOI: 10.1088/1741-2552/ab9dba] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Real-time approaches for transcranial magnetic stimulation (TMS) based on a specific EEG phase are a promising avenue for more precise neuromodulation interventions. However, optimal approaches to reliably extract the EEG phase in a frequency band of interest to inform TMS are still to be identified. Here, we implement a new real-time phase detection method for closed-loop EEG-TMS for robust phase extraction. We compare this algorithm with state-of-the-art methods and evaluate its performance both in silico and experimentally. APPROACH We propose a new robust algorithm (Educated Temporal Prediction) for delivering real-time EEG phase-specific stimulation based on short prerecorded EEG training data. This method estimates the interpeak period from a training period and applies a bias correction to predict future peaks. We compare the accuracy and computation speed of the ETP algorithm with two existing methods (Fourier based, Autoregressive Prediction) using prerecorded resting EEG data and real-time experiments. MAIN RESULTS We found that Educated Temporal Prediction performs with higher accuracy than Fourier-based or Autoregressive methods both in silico and in vivo while being computationally more efficient. Further, we document the dependency of the EEG signal-to-noise ratio (SNR) on algorithm accuracy across all algorithms. SIGNIFICANCE Our results give important insights for real-time EEG-TMS technical development as well as experimental design. Due to its robustness and computational efficiency, our method can find broad use in experimental research or clinical applications. Through open sharing of code for all three methods, we enable broad access of TMS-EEG real-time algorithms to the community.
Collapse
Affiliation(s)
- Sina Shirinpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | | | | | | |
Collapse
|