1
|
Semenkov I, Fedosov N, Makarov I, Ossadtchi A. Real-time low latency estimation of brain rhythms with deep neural networks. J Neural Eng 2023; 20:056008. [PMID: 37683653 DOI: 10.1088/1741-2552/acf7f3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits.Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects.Main results.The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios>90% rhythm's envelope correlation with<10 ms effective delay and<20∘circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture.Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.
Collapse
Affiliation(s)
- Ilia Semenkov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
| | - Nikita Fedosov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
| | - Ilya Makarov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
| | - Alexei Ossadtchi
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
- LLC 'Life Improvement by Future Technologies Center', Moscow, Russia
| |
Collapse
|
2
|
Kalokairinou L, Specker Sullivan L, Wexler A. Neurofeedback as placebo: a case of unintentional deception? JOURNAL OF MEDICAL ETHICS 2022; 48:1037-1042. [PMID: 34521768 PMCID: PMC9205641 DOI: 10.1136/medethics-2021-107435] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 08/18/2021] [Indexed: 05/27/2023]
Abstract
The use of placebo in clinical practice has been the topic of extensive debate in the bioethics literature, with much scholarship focusing on concerns regarding deception. While considerations of placebo without deception have largely centred on open-label placebo, this paper considers a different kind of ethical quandary regarding placebo without an intent to deceive-one where the provider believes a treatment is effective due to a direct physiological mechanism, even though that belief may not be supported by rigorous scientific evidence. This is often the case with complementary and alternative medicine (CAM) techniques and also with some mainstream therapies that have not proven to be better than sham. Using one such CAM technique as a case study-electroencephalography (EEG) neurofeedback for attention-deficit/hyperactivity disorder (ADHD)-this paper explores the ethics of providing therapies that may have some beneficial effect, although one that is likely due to placebo effect. First, we provide background on EEG neurofeedback for ADHD and its evidence base, showing how it has proven to be equivalent to-but not better than-sham neurofeedback. Subsequently, we explore whether offering therapies that are claimed to work via specific physical pathways, but may actually work due to the placebo effect, constitute deception. We suggest that this practice may constitute unintentional deception regarding mechanism of action. Ultimately, we argue that providers have increased information provision obligations when offering treatments that diverge from standard of care and we make recommendations for mitigating unintentional deception.
Collapse
Affiliation(s)
- Louiza Kalokairinou
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
3
|
Kalokairinou L, Choi R, Nagappan A, Wexler A. Opportunity Cost or Opportunity Lost: An Empirical Assessment of Ethical Concerns and Attitudes of EEG Neurofeedback Users. NEUROETHICS-NETH 2022; 15:28. [PMID: 36249541 PMCID: PMC9555209 DOI: 10.1007/s12152-022-09506-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/15/2022] [Indexed: 10/14/2022]
Abstract
Background Electroencephalography (EEG) neurofeedback is a type of biofeedback that purportedly teaches users how to control their brainwaves. Although neurofeedback is currently offered by thousands of providers worldwide, its provision is contested, as its effectiveness beyond a placebo effect is unproven. While scholars have voiced numerous ethical concerns about neurofeedback-regarding opportunity cost, physical and psychological harms, financial cost, and informed consent-to date these concerns have remained theoretical. This pilot study aimed to provide insights on whether these issues were supported by empirical data from the experiences of neurofeedback users. Methods Semi-structured telephone interviews were conducted with individuals who had used EEG neurofeedback for themselves and/or for a child. Results The majority of respondents (N = 36) were female (75%), white (92%), and of higher socioeconomic status relative to the U.S. population. Among adult users (n = 33), most (78.8%) resorted to neurofeedback after having tried other therapies and were satisfied with treatment (81.8%). The majority paid for neurofeedback out-of-pocket (72.7%) and considered it to be good value for money (84.8%). More than half (57.6%) considered neurofeedback to be a scientifically well-established therapy. However, of those, 78.9%were using neurofeedback for indications not adequately supported by scientific evidence. Conclusion Concerns regarding opportunity cost, physical and psychological harms, and financial cost are not substantiated by our findings. Our results partially support concerns regarding insufficient understanding of limitations. This study underlines the disconnect between some of the theoretical concerns raised by scholars regarding the use of non-validated therapies and the lived experiences of users.
Collapse
Affiliation(s)
- Louiza Kalokairinou
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Rebekah Choi
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ashwini Nagappan
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
4
|
Mirifar A, Keil A, Ehrlenspiel F. Neurofeedback and neural self-regulation: a new perspective based on allostasis. Rev Neurosci 2022; 33:607-629. [PMID: 35122709 PMCID: PMC9381001 DOI: 10.1515/revneuro-2021-0133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/13/2022] [Indexed: 11/15/2022]
Abstract
The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.
Collapse
Affiliation(s)
- Arash Mirifar
- Department of Sport and Health Sciences, Chair of Sport Psychology, Technische Universität München, Munich, Bavaria, Germany
- Institute of Sports Science, Leibniz UniversityHannover, Germany
| | - Andreas Keil
- Center for the Study of Emotion & Attention, University of Florida, Gainesville, Florida, United States of America
| | - Felix Ehrlenspiel
- Department of Sport and Health Sciences, Chair of Sport Psychology, Technische Universität München, Munich, Bavaria, Germany
| |
Collapse
|
5
|
Zhou Q, Cheng R, Yao L, Ye X, Xu K. Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface. Front Hum Neurosci 2022; 16:831995. [PMID: 35463935 PMCID: PMC9026187 DOI: 10.3389/fnhum.2022.831995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/16/2022] [Indexed: 01/03/2023] Open
Abstract
Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.
Collapse
Affiliation(s)
- Qing Zhou
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China
- Zhejiang Lab, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou, China
| | - Ruidong Cheng
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
| | - Lin Yao
- MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China
- Department of Neurobiology, Affiliated Mental Health Center and Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The College of Computer Science, Zhejiang University, Hangzhou, China
| | - Xiangming Ye
- Center for Rehabilitation Medicine, Rehabilitation and Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China
- Xiangming Ye,
| | - Kedi Xu
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China
- Zhejiang Lab, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou, China
- MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China
- *Correspondence: Kedi Xu,
| |
Collapse
|
6
|
Grosselin F, Breton A, Yahia-Cherif L, Wang X, Spinelli G, Hugueville L, Fossati P, Attal Y, Navarro-Sune X, Chavez M, George N. Alpha activity neuromodulation induced by individual alpha-based neurofeedback learning in ecological context: a double-blind randomized study. Sci Rep 2021; 11:18489. [PMID: 34531416 PMCID: PMC8445968 DOI: 10.1038/s41598-021-96893-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 08/06/2021] [Indexed: 02/08/2023] Open
Abstract
The neuromodulation induced by neurofeedback training (NFT) remains a matter of debate. Investigating the modulation of brain activity specifically associated with NF requires controlling for multiple factors, such as reward, performance, congruency between task and targeted brain activity. This can be achieved using sham feedback (FB) control condition, equating all aspects of the experiment but the link between brain activity and FB. We aimed at investigating the modulation of individual alpha EEG activity induced by NFT in a double-blind, randomized, sham-controlled study. Forty-eight healthy participants were assigned to either NF (n = 25) or control (n = 23) group and performed alpha upregulation training (over 12 weeks) with a wearable EEG device. Participants of the NF group received FB based on their individual alpha activity. The control group received the auditory FB of participants of the NF group. An increase of alpha activity across training sessions was observed in the NF group only (p < 0.001). This neuromodulation was selective in that there was no evidence for similar effects in the theta (4-8 Hz) and low beta (13-18 Hz) bands. While alpha upregulation was found in the NF group only, psychological outcome variables showed overall increased feeling of control, decreased anxiety level and increased relaxation feeling, without any significant difference between the NF and the control groups. This is interpreted in terms of learning context and placebo effects. Our results pave the way to self-learnt, NF-based neuromodulation with light-weighted, wearable EEG systems.
Collapse
Affiliation(s)
- Fanny Grosselin
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute (ICM), INSERM U 1127, CNRS UMR 7225, Equipe Aramis, 75013, Paris, France.
- myBrain Technologies, 75010, Paris, France.
- INRIA, Aramis Project-Team, 75013, Paris, France.
| | | | - Lydia Yahia-Cherif
- Institut du Cerveau-Paris Brain Institute-ICM, Centre MEG-EEG, Paris, France
- CNRS, UMR 7225, F-75013, Paris, France
- Inserm, U 1127, Paris, France
- Sorbonne Université, Paris, France
| | - Xi Wang
- myBrain Technologies, 75010, Paris, France
| | | | - Laurent Hugueville
- Institut du Cerveau-Paris Brain Institute-ICM, Centre MEG-EEG, Paris, France
- CNRS, UMR 7225, F-75013, Paris, France
- Inserm, U 1127, Paris, France
- Sorbonne Université, Paris, France
| | - Philippe Fossati
- CNRS, UMR 7225, F-75013, Paris, France
- Inserm, U 1127, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau-Paris Brain Institute-ICM, Equipe CIA-Cognitive Control, Interoception, Attention, 75013, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Service de Psychiatrie Adulte, 75013, Paris, France
| | | | | | | | - Nathalie George
- Institut du Cerveau-Paris Brain Institute-ICM, Centre MEG-EEG, Paris, France
- CNRS, UMR 7225, F-75013, Paris, France
- Inserm, U 1127, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau-Paris Brain Institute-ICM, Equipe Experimental Neurosurgery, 75013, Paris, France
| |
Collapse
|
7
|
Patel K, Henshaw J, Sutherland H, Taylor JR, Casson AJ, Lopez-Diaz K, Brown CA, Jones AKP, Sivan M, Trujillo-Barreto NJ. Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain. Front Neurosci 2021; 14:620666. [PMID: 33732101 PMCID: PMC7958977 DOI: 10.3389/fnins.2020.620666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/22/2020] [Indexed: 11/29/2022] Open
Abstract
Objective Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful. Here, we quantify the changes during α-NFB for chronic pain in terms of dynamic changes in alpha states. Methods Four chronic pain and four healthy participants received five NFB sessions designed to increase frontal alpha power. Changes in pain resilience were measured using visual analogue scale (VAS) during repeated cold-pressor tests (CPT). Changes in alpha state static and dynamic parameters such as fractional occupancy (time in high alpha state), dwell time (length of high alpha state) and transition probability (probability of moving from low to high alpha state) were analyzed using Friedman’s Test and correlated with changes in pain scores using Pearson’s correlation. Results There was no significant change in mean frontal alpha power during NFB. There was a trend of an increase in fractional occupancy, mean dwell duration and transition probability of high alpha state over the five sessions in chronic pain patients only. Significant correlations were observed between change in pain scores and fractional occupancy (r = −0.45, p = 0.03), mean dwell time (r = -0.48, p = 0.04) and transition probability from a low to high state (r = -0.47, p = 0.03) in chronic pain patients but not in healthy participants. Conclusion There is a differential effect between patients and healthy participants in terms of correlation between change in pain scores and alpha state parameters. Parameters providing a more precise description of the alpha power dynamics than the mean may help understand the therapeutic effect of neurofeedback on chronic pain.
Collapse
Affiliation(s)
- Kajal Patel
- School of Medicine, University of Manchester, Manchester, United Kingdom.,Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - James Henshaw
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Heather Sutherland
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Jason R Taylor
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Alexander J Casson
- Department of Electrical and Electronic Engineering, University of Manchester, Manchester, United Kingdom
| | - Karen Lopez-Diaz
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Christopher A Brown
- Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Anthony K P Jones
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| | - Manoj Sivan
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom.,Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, United Kingdom
| | - Nelson J Trujillo-Barreto
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
| |
Collapse
|
8
|
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
|
9
|
Smetanin N, Belinskaya A, Lebedev M, Ossadtchi A. Digital filters for low-latency quantification of brain rhythms in real time. J Neural Eng 2020; 17:046022. [PMID: 32289760 DOI: 10.1088/1741-2552/ab890f] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off. APPROACH Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples. MAIN RESULTS When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold. SIGNIFICANCE The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.
Collapse
Affiliation(s)
- Nikolai Smetanin
- Center for Bioelectric Interfaces, Higher School of Economics, Moscow, 101000, Russia
| | | | | | | |
Collapse
|
10
|
Jones SR, Sliva DD. Is Alpha Asymmetry a Byproduct or Cause of Spatial Attention? New Evidence Alpha Neurofeedback Controls Measures of Spatial Attention. Neuron 2020; 105:404-406. [PMID: 32027830 DOI: 10.1016/j.neuron.2019.12.033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Cued spatial attention differentially modulates alpha power in attended relative to non-attended brain representations, termed the alpha asymmetry. Yet a causal role for alpha in attention is debated. In this issue of Neuron, Bagherzadeh et al., (2019) utilize neurofeedback to train alpha asymmetry and causally impact measures of spatial attention.
Collapse
Affiliation(s)
- Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI 02912, USA; Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI 02908, USA.
| | - Danielle D Sliva
- Department of Neuroscience, Brown University, Providence, RI 02912, USA.
| |
Collapse
|
11
|
Smetanin N, Volkova K, Zabodaev S, Lebedev MA, Ossadtchi A. NFBLab-A Versatile Software for Neurofeedback and Brain-Computer Interface Research. Front Neuroinform 2018; 12:100. [PMID: 30618704 PMCID: PMC6311652 DOI: 10.3389/fninf.2018.00100] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 12/12/2018] [Indexed: 11/13/2022] Open
Abstract
Neurofeedback (NFB) is a real-time paradigm, where subjects learn to volitionally modulate their own brain activity recorded with electroencephalographic (EEG), magnetoencephalographic (MEG) or other functional brain imaging techniques and presented to them via one of sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. Although the early NFB studies date back nearly six decades ago, there is still much debate regarding the efficiency of this approach and the ways it should be implemented. Partly, the existing controversy is due to suboptimal conditions under which the NFB training is undertaken. Therefore, new experimental tools attempting to provide optimal or close to optimal training conditions are needed to further exploration of NFB paradigms and comparison of their effects across subjects and training days. To this end, we have developed open-source NFBLab, a versatile, Python-based software for conducting NFB experiments with completely reproducible paradigms and low-latency feedback presentation. Complex experimental protocols can be configured using the GUI and saved in NFBLab's internal XML-based language that describes signal processing tracts, experimental blocks and sequences including randomization of experimental blocks. NFBLab implements interactive modules that enable individualized EEG/MEG signal processing tracts specification using spatial and temporal filters for feature selection and artifacts removal. NFBLab supports direct interfacing to MNE-Python software to facilitate source-space NFB based on individual head models and properly tailored individual inverse solvers. In addition to the standard algorithms for extraction of brain rhythms dynamics from EEG and MEG data, NFBLab implements several novel in-house signal processing algorithms that afford significant reduction in latency of feedback presentation and may potentially improve training effects. The software also supports several standard BCI paradigms. To interface with external data acquisition devices NFBLab employs Lab Streaming Layer protocol supported by the majority of EEG vendors. MEG devices are interfaced through the Fieldtrip buffer.
Collapse
Affiliation(s)
- Nikolai Smetanin
- Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow, Russia
| | - Ksenia Volkova
- Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow, Russia
| | | | - Mikhail A Lebedev
- Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow, Russia
| | - Alexei Ossadtchi
- Center for Bioelectric Interfaces, National Research University Higher School of Economics, Moscow, Russia
| |
Collapse
|