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Asaadi AH, Amiri SH, Bosaghzadeh A, Ebrahimpour R. Effects and prediction of cognitive load on encoding model of brain response to auditory and linguistic stimuli in educational multimedia. Sci Rep 2024; 14:9133. [PMID: 38644370 PMCID: PMC11033259 DOI: 10.1038/s41598-024-59411-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 04/10/2024] [Indexed: 04/23/2024] Open
Abstract
Multimedia is extensively used for educational purposes. However, certain types of multimedia lack proper design, which could impose a cognitive load on the user. Therefore, it is essential to predict cognitive load and understand how it impairs brain functioning. Participants watched a version of educational multimedia that applied Mayer's principles, followed by a version that did not. Meanwhile, their electroencephalography (EEG) was recorded. Subsequently, they participated in a post-test and completed a self-reported cognitive load questionnaire. The audio envelope and word frequency were extracted from the multimedia, and the temporal response functions (TRFs) were obtained using a linear encoding model. We observed that the behavioral data are different between the two groups and the TRFs of the two multimedia versions were different. We saw changes in the amplitude and latencies of both early and late components. In addition, correlations were found between behavioral data and the amplitude and latencies of TRF components. Cognitive load decreased participants' attention to the multimedia, and semantic processing of words also occurred with a delay and smaller amplitude. Hence, encoding models provide insights into the temporal and spatial mapping of the cognitive load activity, which could help us detect and reduce cognitive load in potential environments such as educational multimedia or simulators for different purposes.
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Affiliation(s)
- Amir Hosein Asaadi
- Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of Iran
- Institute for Research in Fundamental Sciences (IPM), School of Cognitive Sciences, Tehran, Iran
| | - S Hamid Amiri
- Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of Iran
| | - Alireza Bosaghzadeh
- Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of Iran
| | - Reza Ebrahimpour
- Center for Cognitive Science, Institute for Convergence Science and Technology (ICST), Sharif University of Technology, P.O. Box:14588-89694, Tehran, Iran.
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2
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Shi N, Miao Y, Huang C, Li X, Song Y, Chen X, Wang Y, Gao X. Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface. Neuroimage 2024; 289:120548. [PMID: 38382863 DOI: 10.1016/j.neuroimage.2024.120548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/16/2024] [Accepted: 02/18/2024] [Indexed: 02/23/2024] Open
Abstract
An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems.
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Affiliation(s)
- Nanlin Shi
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yining Miao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Changxing Huang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiang Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yonghao Song
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiaogang Chen
- Institute of Biomedical Engineering, Chinese Academy of Medical, Sciences and Peking Union Medical College, Street, Tianjin 300192, China
| | - Yijun Wang
- Key Laboratory of Solid-State Optoelectronics Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
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3
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Panela RA, Copelli F, Herrmann B. Reliability and generalizability of neural speech tracking in younger and older adults. Neurobiol Aging 2024; 134:165-180. [PMID: 38103477 DOI: 10.1016/j.neurobiolaging.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/09/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023]
Abstract
Neural tracking of spoken speech is considered a potential clinical biomarker for speech-processing difficulties, but the reliability of neural speech tracking is unclear. Here, younger and older adults listened to stories in two sessions while electroencephalography was recorded to investigate the reliability and generalizability of neural speech tracking. Speech tracking amplitude was larger for older than younger adults, consistent with an age-related loss of inhibition. The reliability of neural speech tracking was moderate (ICC ∼0.5-0.75) and tended to be higher for older adults. However, reliability was lower for speech tracking than for neural responses to noise bursts (ICC >0.8), which we used as a benchmark for maximum reliability. Neural speech tracking generalized moderately across different stories (ICC ∼0.5-0.6), which appeared greatest for audiobook-like stories spoken by the same person. Hence, a variety of stories could possibly be used for clinical assessments. Overall, the current data are important for developing a biomarker of speech processing but suggest that further work is needed to increase the reliability to meet clinical standards.
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Affiliation(s)
- Ryan A Panela
- Rotman Research Institute, Baycrest Academy for Research and Education, M6A 2E1 North York, ON, Canada; Department of Psychology, University of Toronto, M5S 1A1 Toronto, ON, Canada
| | - Francesca Copelli
- Rotman Research Institute, Baycrest Academy for Research and Education, M6A 2E1 North York, ON, Canada; Department of Psychology, University of Toronto, M5S 1A1 Toronto, ON, Canada
| | - Björn Herrmann
- Rotman Research Institute, Baycrest Academy for Research and Education, M6A 2E1 North York, ON, Canada; Department of Psychology, University of Toronto, M5S 1A1 Toronto, ON, Canada.
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4
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Zhang X, Li J, Li Z, Hong B, Diao T, Ma X, Nolte G, Engel AK, Zhang D. Leading and following: Noise differently affects semantic and acoustic processing during naturalistic speech comprehension. Neuroimage 2023; 282:120404. [PMID: 37806465 DOI: 10.1016/j.neuroimage.2023.120404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/19/2023] [Accepted: 10/05/2023] [Indexed: 10/10/2023] Open
Abstract
Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and decreasing speech comprehension and intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments.
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Affiliation(s)
- Xinmiao Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China; Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Jiawei Li
- Department of Education and Psychology, Freie Universität Berlin, Berlin 14195, Federal Republic of Germany
| | - Zhuoran Li
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China; Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Bo Hong
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Tongxiang Diao
- Department of Otolaryngology, Head and Neck Surgery, Peking University, People's Hospital, Beijing 100044, China
| | - Xin Ma
- Department of Otolaryngology, Head and Neck Surgery, Peking University, People's Hospital, Beijing 100044, China
| | - Guido Nolte
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Federal Republic of Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Federal Republic of Germany
| | - Dan Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China; Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China.
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Lindboom E, Nidiffer A, Carney LH, Lalor EC. Incorporating models of subcortical processing improves the ability to predict EEG responses to natural speech. Hear Res 2023; 433:108767. [PMID: 37060895 PMCID: PMC10559335 DOI: 10.1016/j.heares.2023.108767] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/29/2023] [Accepted: 04/09/2023] [Indexed: 04/17/2023]
Abstract
The goal of describing how the human brain responds to complex acoustic stimuli has driven auditory neuroscience research for decades. Often, a systems-based approach has been taken, in which neurophysiological responses are modeled based on features of the presented stimulus. This includes a wealth of work modeling electroencephalogram (EEG) responses to complex acoustic stimuli such as speech. Examples of the acoustic features used in such modeling include the amplitude envelope and spectrogram of speech. These models implicitly assume a direct mapping from stimulus representation to cortical activity. However, in reality, the representation of sound is transformed as it passes through early stages of the auditory pathway, such that inputs to the cortex are fundamentally different from the raw audio signal that was presented. Thus, it could be valuable to account for the transformations taking place in lower-order auditory areas, such as the auditory nerve, cochlear nucleus, and inferior colliculus (IC) when predicting cortical responses to complex sounds. Specifically, because IC responses are more similar to cortical inputs than acoustic features derived directly from the audio signal, we hypothesized that linear mappings (temporal response functions; TRFs) fit to the outputs of an IC model would better predict EEG responses to speech stimuli. To this end, we modeled responses to the acoustic stimuli as they passed through the auditory nerve, cochlear nucleus, and inferior colliculus before fitting a TRF to the output of the modeled IC responses. Results showed that using model-IC responses in traditional systems analyzes resulted in better predictions of EEG activity than using the envelope or spectrogram of a speech stimulus. Further, it was revealed that model-IC derived TRFs predict different aspects of the EEG than acoustic-feature TRFs, and combining both types of TRF models provides a more accurate prediction of the EEG response.
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Affiliation(s)
- Elsa Lindboom
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA
| | - Aaron Nidiffer
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA
| | - Laurel H Carney
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA; Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA.
| | - Edmund C Lalor
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY, USA
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6
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Chen YP, Schmidt F, Keitel A, Rösch S, Hauswald A, Weisz N. Speech intelligibility changes the temporal evolution of neural speech tracking. Neuroimage 2023; 268:119894. [PMID: 36693596 DOI: 10.1016/j.neuroimage.2023.119894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 12/13/2022] [Accepted: 01/20/2023] [Indexed: 01/22/2023] Open
Abstract
Listening to speech with poor signal quality is challenging. Neural speech tracking of degraded speech has been used to advance the understanding of how brain processes and speech intelligibility are interrelated. However, the temporal dynamics of neural speech tracking and their relation to speech intelligibility are not clear. In the present MEG study, we exploited temporal response functions (TRFs), which has been used to describe the time course of speech tracking on a gradient from intelligible to unintelligible degraded speech. In addition, we used inter-related facets of neural speech tracking (e.g., speech envelope reconstruction, speech-brain coherence, and components of broadband coherence spectra) to endorse our findings in TRFs. Our TRF analysis yielded marked temporally differential effects of vocoding: ∼50-110 ms (M50TRF), ∼175-230 ms (M200TRF), and ∼315-380 ms (M350TRF). Reduction of intelligibility went along with large increases of early peak responses M50TRF, but strongly reduced responses in M200TRF. In the late responses M350TRF, the maximum response occurred for degraded speech that was still comprehensible then declined with reduced intelligibility. Furthermore, we related the TRF components to our other neural "tracking" measures and found that M50TRF and M200TRF play a differential role in the shifting center frequency of the broadband coherence spectra. Overall, our study highlights the importance of time-resolved computation of neural speech tracking and decomposition of coherence spectra and provides a better understanding of degraded speech processing.
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Affiliation(s)
- Ya-Ping Chen
- Centre for Cognitive Neuroscience, University of Salzburg, 5020 Salzburg, Austria; Department of Psychology, University of Salzburg, 5020 Salzburg, Austria.
| | - Fabian Schmidt
- Centre for Cognitive Neuroscience, University of Salzburg, 5020 Salzburg, Austria; Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Anne Keitel
- Psychology, School of Social Sciences, University of Dundee, DD1 4HN Dundee, UK
| | - Sebastian Rösch
- Department of Otorhinolaryngology, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Anne Hauswald
- Centre for Cognitive Neuroscience, University of Salzburg, 5020 Salzburg, Austria; Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
| | - Nathan Weisz
- Centre for Cognitive Neuroscience, University of Salzburg, 5020 Salzburg, Austria; Department of Psychology, University of Salzburg, 5020 Salzburg, Austria; Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, 5020 Salzburg, Austria
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7
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Carta S, Mangiacotti AMA, Valdes AL, Reilly RB, Franco F, Di Liberto GM. The impact of temporal synchronisation imprecision on TRF analyses. J Neurosci Methods 2023; 385:109765. [PMID: 36481165 DOI: 10.1016/j.jneumeth.2022.109765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/17/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Sara Carta
- ADAPT Centre, Trinity College, The University of Dublin, Ireland; School of Computer Science and Statistics, Trinity College, The University of Dublin, Ireland
| | - Anthony M A Mangiacotti
- Department of Psychology, Middlesex University, London, United Kingdom; FISPPA Department, University of Padova, Padova, Italy
| | - Alejandro Lopez Valdes
- Trinity Centre for Biomedical Engineering, Trinity College, The University of Dublin, Ireland; Global Brain Health Institute, Trinity College, The University of Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College, The University of Dublin, Ireland; School of Engineering, Trinity College, The University of Dublin, Ireland
| | - Richard B Reilly
- Trinity Centre for Biomedical Engineering, Trinity College, The University of Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College, The University of Dublin, Ireland; School of Engineering, Trinity College, The University of Dublin, Ireland; School of Medicine, Trinity College, The University of Dublin, Ireland
| | - Fabia Franco
- Department of Psychology, Middlesex University, London, United Kingdom
| | - Giovanni M Di Liberto
- ADAPT Centre, Trinity College, The University of Dublin, Ireland; School of Computer Science and Statistics, Trinity College, The University of Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College, The University of Dublin, Ireland.
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8
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Jessen S, Obleser J, Tune S. Neural tracking in infants - An analytical tool for multisensory social processing in development. Dev Cogn Neurosci 2021; 52:101034. [PMID: 34781250 PMCID: PMC8593584 DOI: 10.1016/j.dcn.2021.101034] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/09/2021] [Accepted: 11/07/2021] [Indexed: 11/18/2022] Open
Abstract
Humans are born into a social environment and from early on possess a range of abilities to detect and respond to social cues. In the past decade, there has been a rapidly increasing interest in investigating the neural responses underlying such early social processes under naturalistic conditions. However, the investigation of neural responses to continuous dynamic input poses the challenge of how to link neural responses back to continuous sensory input. In the present tutorial, we provide a step-by-step introduction to one approach to tackle this issue, namely the use of linear models to investigate neural tracking responses in electroencephalographic (EEG) data. While neural tracking has gained increasing popularity in adult cognitive neuroscience over the past decade, its application to infant EEG is still rare and comes with its own challenges. After introducing the concept of neural tracking, we discuss and compare the use of forward vs. backward models and individual vs. generic models using an example data set of infant EEG data. Each section comprises a theoretical introduction as well as a concrete example using MATLAB code. We argue that neural tracking provides a promising way to investigate early (social) processing in an ecologically valid setting.
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Affiliation(s)
- Sarah Jessen
- Department of Neurology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Center of Brain, Behavior, and Metabolism, University of Lübeck, Germany.
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Center of Brain, Behavior, and Metabolism, University of Lübeck, Germany
| | - Sarah Tune
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Center of Brain, Behavior, and Metabolism, University of Lübeck, Germany.
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Das N, Vanthornhout J, Francart T, Bertrand A. Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. Neuroimage 2020; 204:116211. [PMID: 31546052 PMCID: PMC7355237 DOI: 10.1016/j.neuroimage.2019.116211] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 08/30/2019] [Accepted: 09/17/2019] [Indexed: 12/21/2022] Open
Abstract
A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive techniques like magneto- or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials. Therefore, we present a data-driven method that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method first estimates the stimulus-driven neural response using the given stimulus, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus using EEG, our method resulted in more accurate short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.
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Affiliation(s)
- Neetha Das
- Dept. Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium; Dept. Neurosciences, ExpORL, KU Leuven, Herestraat 49 Bus 721, B-3000, Leuven, Belgium.
| | - Jonas Vanthornhout
- Dept. Neurosciences, ExpORL, KU Leuven, Herestraat 49 Bus 721, B-3000, Leuven, Belgium
| | - Tom Francart
- Dept. Neurosciences, ExpORL, KU Leuven, Herestraat 49 Bus 721, B-3000, Leuven, Belgium
| | - Alexander Bertrand
- Dept. Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium.
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10
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Jessen S, Fiedler L, Münte TF, Obleser J. Quantifying the individual auditory and visual brain response in 7-month-old infants watching a brief cartoon movie. Neuroimage 2019; 202:116060. [PMID: 31362048 DOI: 10.1016/j.neuroimage.2019.116060] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/05/2019] [Accepted: 07/26/2019] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) continues to be the most popular method to investigate cognitive brain mechanisms in young children and infants. Most infant studies rely on the well-established and easy-to-use event-related brain potential (ERP). As a severe disadvantage, ERP computation requires a large number of repetitions of items from the same stimulus-category, compromising both ERPs' reliability and their ecological validity in infant research. We here explore a way to investigate infant continuous EEG responses to an ongoing, engaging signal (i.e., "neural tracking") by using multivariate temporal response functions (mTRFs), an approach increasingly popular in adult EEG research. N = 52 infants watched a 5-min episode of an age-appropriate cartoon while the EEG signal was recorded. We estimated and validated forward encoding models of auditory-envelope and visual-motion features. We compared individual and group-based ('generic') models of the infant brain response to comparison data from N = 28 adults. The generic model yielded clearly defined response functions for both, the auditory and the motion regressor. Importantly, this response profile was present also on an individual level, albeit with lower precision of the estimate but above-chance predictive accuracy for the modelled individual brain responses. In sum, we demonstrate that mTRFs are a feasible way of analyzing continuous EEG responses in infants. We observe robust response estimates both across and within participants from only 5 min of recorded EEG signal. Our results open ways for incorporating more engaging and more ecologically valid stimulus materials when probing cognitive, perceptual, and affective processes in infants and young children.
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Affiliation(s)
- Sarah Jessen
- Department of Neurology, University of Lübeck, Lübeck, Germany.
| | - Lorenz Fiedler
- Department of Psychology, University of Lübeck, Lübeck, Germany
| | - Thomas F Münte
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Lübeck, Germany
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11
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Abstract
Storing temporal sequences of events (i.e., sequence memory) is fundamental to many cognitive functions. However, it is unknown how the sequence order information is maintained and represented in working memory and its behavioral significance, particularly in human subjects. We recorded electroencephalography (EEG) in combination with a temporal response function (TRF) method to dissociate item-specific neuronal reactivations. We demonstrate that serially remembered items are successively reactivated during memory retention. The sequential replay displays two interesting properties compared to the actual sequence. First, the item-by-item reactivation is compressed within a 200 – 400 ms window, suggesting that external events are associated within a plasticity-relevant window to facilitate memory consolidation. Second, the replay is in a temporally reversed order and is strongly related to the recency effect in behavior. This fast-backward replay, previously revealed in rat hippocampus and demonstrated here in human cortical activities, might constitute a general neural mechanism for sequence memory and learning. Have you ever played the ‘Memory Maze Challenge’ game, or its predecessor from the 1980s, ‘Simon’? Players must memorize a sequence of colored lights, and then reproduce the sequence by tapping the colors on a pad. The sequence becomes longer with each trial, making the task more and more difficult. One wrong response and the game is over. Storing and retrieving sequences is key to many cognitive processes, from following speech to hitting a tennis ball to recalling what you did last week. Such tasks require memorizing the order in which items occur as well as the items themselves. But how do we hold this information in memory? Huang et al. reveal the answer by using scalp electrodes to record the brain activity of healthy volunteers as they memorize and then recall a sequence. Memorizing, or encoding, each of the items in the sequence triggered a distinct pattern of brain activity. As the volunteers held the sequence in memory, their brains replayed these activity patterns one after the other. But this replay showed two non-intuitive features. First, it was speeded up relative to the original encoding. In fact, the brain compressed the entire sequence into about 200 to 400 milliseconds. Second, the brain replayed the sequence backwards. The activity pattern corresponding to the last item was replayed first, while that corresponding to the first item was replayed last. This ‘fast-backward’ replay may explain why we tend to recall items at the end of a list better than those in the middle, a phenomenon known as the recency effect. The results of Huang et al. suggest that when we hold a list of items in memory, the brain does not replay the list in its original form, like an echo. Instead, the brain restructures and reorganizes the list, compressing and reversing it. This process, which is also seen in rodents, helps the brain to incorporate the list of items into existing neuronal networks for memory storage.
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Affiliation(s)
- Qiaoli Huang
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Jianrong Jia
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Qiming Han
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Huan Luo
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
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Brodbeck C, Presacco A, Simon JZ. Neural source dynamics of brain responses to continuous stimuli: Speech processing from acoustics to comprehension. Neuroimage 2018; 172:162-174. [PMID: 29366698 PMCID: PMC5910254 DOI: 10.1016/j.neuroimage.2018.01.042] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 12/12/2017] [Accepted: 01/17/2018] [Indexed: 11/24/2022] Open
Abstract
Human experience often involves continuous sensory information that unfolds over time. This is true in particular for speech comprehension, where continuous acoustic signals are processed over seconds or even minutes. We show that brain responses to such continuous stimuli can be investigated in detail, for magnetoencephalography (MEG) data, by combining linear kernel estimation with minimum norm source localization. Previous research has shown that the requirement to average data over many trials can be overcome by modeling the brain response as a linear convolution of the stimulus and a kernel, or response function, and estimating a kernel that predicts the response from the stimulus. However, such analysis has been typically restricted to sensor space. Here we demonstrate that this analysis can also be performed in neural source space. We first computed distributed minimum norm current source estimates for continuous MEG recordings, and then computed response functions for the current estimate at each source element, using the boosting algorithm with cross-validation. Permutation tests can then assess the significance of individual predictor variables, as well as features of the corresponding spatio-temporal response functions. We demonstrate the viability of this technique by computing spatio-temporal response functions for speech stimuli, using predictor variables reflecting acoustic, lexical and semantic processing. Results indicate that processes related to comprehension of continuous speech can be differentiated anatomically as well as temporally: acoustic information engaged auditory cortex at short latencies, followed by responses over the central sulcus and inferior frontal gyrus, possibly related to somatosensory/motor cortex involvement in speech perception; lexical frequency was associated with a left-lateralized response in auditory cortex and subsequent bilateral frontal activity; and semantic composition was associated with bilateral temporal and frontal brain activity. We conclude that this technique can be used to study the neural processing of continuous stimuli in time and anatomical space with the millisecond temporal resolution of MEG. This suggests new avenues for analyzing neural processing of naturalistic stimuli, without the necessity of averaging over artificially short or truncated stimuli.
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Affiliation(s)
- Christian Brodbeck
- Institute for Systems Research, University of Maryland, College Park, MD, USA.
| | | | - Jonathan Z Simon
- Institute for Systems Research, University of Maryland, College Park, MD, USA; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Department of Biology, University of Maryland, College Park, MD, USA
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