1
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Yu RWL, Chan AHS. Effects of player-video game interaction on the mental effort of older adults with the use of electroencephalography and NASA-TLX. Arch Gerontol Geriatr 2024; 124:105442. [PMID: 38676979 DOI: 10.1016/j.archger.2024.105442] [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: 09/25/2022] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/29/2024]
Abstract
While player-video game interaction appears to affect older adults in gaming, there is limited knowledge regarding the cognitive demands associated with the anticipation of performing a button press, specifically focusing on the input and game elements relation (I/E relation) in game environments. The study aims to investigate the effects of lateral and rotational displacement amplitudes of game elements, triggered by a single button-press, on the cognitive effort of older adults. Both subjective and objective measurement methods were employed to assess these effects. A total of 48 older adults participated in three casual video game tasks encompassing lateral and rotational displacements at varying I/E relations (low, medium, and high). Results obtained from the NASA Task Load Index and electroencephalography (EEG) measurements revealed significant differences between the I/E relations. Specifically, the subjective rating of cognitive demand among older players was significantly impacted by a small rotation angle associated with a button press, leading to increased mental, physical, and temporal demands, along with decreased performance. Surprisingly, the analysis of EEG data, particularly the theta-alpha ratio, revealed significant interaction effects of I/E relations, button press type, and game type on the cognitive demand required during gameplay. These findings offer practical implications and point towards future avenues for developing player-video game interactions that are more cognitively friendly for older players in gaming environments.
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Affiliation(s)
- R W L Yu
- Department of Systems Engineering, City University of Hong Kong, Hong Kong, China.
| | - A H S Chan
- Department of Systems Engineering, City University of Hong Kong, Hong Kong, China; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
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2
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Kulke L. Coregistration of EEG and eye-tracking in infants and developing populations. Atten Percept Psychophys 2024:10.3758/s13414-024-02857-y. [PMID: 38388851 DOI: 10.3758/s13414-024-02857-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/06/2024] [Indexed: 02/24/2024]
Abstract
Infants cannot be instructed where to look; therefore, infant researchers rely on observation of their participant's gaze to make inferences about their cognitive processes. They therefore started studying infant attention in the real world from early on. Developmental researchers were early adopters of methods combining observations of gaze and behaviour with electroencephalography (EEG) to study attention and other cognitive functions. However, the direct combination of eye-tracking methods and EEG to test infants is still rare, as it includes specific challenges. The current article reviews the development of co-registration research in infancy. It points out specific challenges of co-registration in infant research and suggests ways to overcome them. It ends with recommendations for implementing the co-registration of EEG and eye-tracking in infant research to maximise the benefits of the two measures and their combination and to orient on Open Science principles while doing so. In summary, this work shows that the co-registration of EEG and eye-tracking in infant research can be beneficial to studying natural and real-world behaviour despite its challenges.
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Affiliation(s)
- Louisa Kulke
- Department of Developmental Psychology with Educational Psychology, University of Bremen, Hochschulring 18, 28359, Bremen, Germany.
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3
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Bouwer FL, Háden GP, Honing H. Probing Beat Perception with Event-Related Potentials (ERPs) in Human Adults, Newborns, and Nonhuman Primates. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1455:227-256. [PMID: 38918355 DOI: 10.1007/978-3-031-60183-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
The aim of this chapter is to give an overview of how the perception of rhythmic temporal regularity such as a regular beat in music can be studied in human adults, human newborns, and nonhuman primates using event-related brain potentials (ERPs). First, we discuss different aspects of temporal structure in general, and musical rhythm in particular, and we discuss the possible mechanisms underlying the perception of regularity (e.g., a beat) in rhythm. Additionally, we highlight the importance of dissociating beat perception from the perception of other types of structure in rhythm, such as predictable sequences of temporal intervals, ordinal structure, and rhythmic grouping. In the second section of the chapter, we start with a discussion of auditory ERPs elicited by infrequent and frequent sounds: ERP responses to regularity violations, such as mismatch negativity (MMN), N2b, and P3, as well as early sensory responses to sounds, such as P1 and N1, have been shown to be instrumental in probing beat perception. Subsequently, we discuss how beat perception can be probed by comparing ERP responses to sounds in regular and irregular sequences, and by comparing ERP responses to sounds in different metrical positions in a rhythm, such as on and off the beat or on strong and weak beats. Finally, we will discuss previous research that has used the aforementioned ERPs and paradigms to study beat perception in human adults, human newborns, and nonhuman primates. In doing so, we consider the possible pitfalls and prospects of the technique, as well as future perspectives.
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Affiliation(s)
- Fleur L Bouwer
- Cognitive Psychology Unit, Institute of Psychology, Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands.
- Department of Psychology, Brain & Cognition, University of Amsterdam, Amsterdam, The Netherlands.
| | - Gábor P Háden
- Institute of Cognitive Neuroscience and Psychology, Budapest, Hungary
- Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Henkjan Honing
- Music Cognition group (MCG), Institute for Logic, Language and Computation (ILLC), Amsterdam Brain and Cognition (ABC), University of Amsterdam, Amsterdam, The Netherlands
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4
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Rayson H, Szul MJ, El-Khoueiry P, Debnath R, Gautier-Martins M, Ferrari PF, Fox N, Bonaiuto JJ. Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood. J Neurosci 2023; 43:8487-8503. [PMID: 37833066 PMCID: PMC10711718 DOI: 10.1523/jneurosci.0886-23.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/15/2023] [Accepted: 07/20/2023] [Indexed: 10/15/2023] Open
Abstract
Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.
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Affiliation(s)
- Holly Rayson
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
- Inovarion, Paris, 75005, France
| | - Maciej J Szul
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Perla El-Khoueiry
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Ranjan Debnath
- Center for Psychiatry and Psychotherapy, Justus-Liebig University, Giessen, 35394, Germany
| | - Marine Gautier-Martins
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Pier F Ferrari
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Nathan Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, 20742
| | - James J Bonaiuto
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
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5
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Schmoigl-Tonis M, Schranz C, Müller-Putz GR. Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review. Front Hum Neurosci 2023; 17:1251690. [PMID: 37920561 PMCID: PMC10619676 DOI: 10.3389/fnhum.2023.1251690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023] Open
Abstract
Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.
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Affiliation(s)
- Mathias Schmoigl-Tonis
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Christoph Schranz
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
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6
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Khazaei M, Raeisi K, Vanhatalo S, Zappasodi F, Comani S, Tokariev A. Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury. Neuroimage 2023; 279:120342. [PMID: 37619792 DOI: 10.1016/j.neuroimage.2023.120342] [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: 03/18/2023] [Revised: 08/11/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.
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Affiliation(s)
- Mohammad Khazaei
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy.
| | - Khadijeh Raeisi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy
| | - Sampsa Vanhatalo
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Filippo Zappasodi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Institute for Advanced Biomedical Technologies, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Anton Tokariev
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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7
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Sinha S, Del Goleto S, Kostova M, Debruille JB. Unveiling the need of interactions for social N400s and supporting the N400 inhibition hypothesis. Sci Rep 2023; 13:12613. [PMID: 37537222 PMCID: PMC10400652 DOI: 10.1038/s41598-023-39345-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/24/2023] [Indexed: 08/05/2023] Open
Abstract
When participants (Pps) are presented with stimuli in the presence of another person, they may consider that person's perspective. Indeed, five recent ERP studies show that the amplitudes of their N400s are increased. The two most recent ones reveal that these social-N400 increases occur even when instructions do not require a focus on the other's perspective. These increases also happen when Pps know that this other person has the same stimulus information as they have. However, in all these works, Pps could see the other person. Here, we tested whether the interaction occurring with this sight is important or whether these social N400 increases also occur when the other person is seated a bit behind Pps, who are aware of it. All had to decide whether the word ending short stories was coherent, incoherent, or equivocal. No social N400 increase was observed: N400s elicited by those words in Pps who were with a confederate (n = 50) were similar to those of Pps who were alone (n = 51). On the other hand, equivocal endings did not elicit larger N400s than coherent ones but triggered larger late posterior positivities (LPPs), like in previous studies. The discussion focuses on the circumstances in which perspective-taking occurs and on the functional significance of the N400 and the LPP.
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Affiliation(s)
- Sujata Sinha
- Department of Neurosciences, Faculty of Medicine, McGill University, Montréal, Canada
- Research Center of the Douglas Mental Health University Institute, Montréal, Canada
| | - Sarah Del Goleto
- UR Paragraphe, Université Paris 8 Vincennes-Saint-Denis, Saint-Denis, France
| | - Milena Kostova
- UR Paragraphe, Université Paris 8 Vincennes-Saint-Denis, Saint-Denis, France
| | - J Bruno Debruille
- Department of Neurosciences, Faculty of Medicine, McGill University, Montréal, Canada.
- Research Center of the Douglas Mental Health University Institute, Montréal, Canada.
- Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Canada.
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8
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Bailey NW, Hill AT, Biabani M, Murphy OW, Rogasch NC, McQueen B, Miljevic A, Fitzgerald PB. RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials. Clin Neurophysiol 2023; 149:202-222. [PMID: 36822996 DOI: 10.1016/j.clinph.2023.01.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 12/20/2022] [Accepted: 01/19/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVE Electroencephalography (EEG) is often used to examine neural activity time-locked to stimuli presentation, referred to as Event-Related Potentials (ERP). However, EEG is influenced by non-neural artifacts, which can confound ERP comparisons. Artifact cleaning reduces artifacts, but often requires time-consuming manual decisions. Most automated methods filter frequencies <1 Hz out of the data, so are not recommended for ERPs (which contain frequencies <1 Hz). Our aim was to test the RELAX (Reduction of Electroencephalographic Artifacts) pre-processing pipeline for use on ERP data. METHODS The cleaning performance of multiple versions of RELAX were compared to four commonly used EEG cleaning pipelines across both artifact cleaning metrics and the amount of variance in ERPs explained by different conditions in a Go-Nogo task. Results RELAX with Multi-channel Wiener Filtering (MWF) and wavelet-enhanced independent component analysis applied to artifacts identified with ICLabel (wICA_ICLabel) cleaned data most effectively and produced amongst the most dependable ERP estimates. RELAX with wICA_ICLabel only or MWF_only may detect effects better for some ERPs. CONCLUSIONS RELAX shows high artifact cleaning performance even when data is high-pass filtered at 0.25 Hz (applicable to ERP analyses). SIGNIFICANCE RELAX is easy to implement via EEGLAB in MATLAB and freely available on GitHub. Given its performance and objectivity we recommend RELAX to improve artifact cleaning and consistency across ERP research.
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Affiliation(s)
- N W Bailey
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, VIC, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Monarch Research Institute Monarch Mental Health Group, Sydney, NSW, Australia.
| | - A T Hill
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, VIC, Australia; Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia
| | - M Biabani
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, VIC, Australia
| | - O W Murphy
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, VIC, Australia; Bionics Institute, East Melbourne, VIC 3002, Australia
| | - N C Rogasch
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, VIC, Australia; Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
| | - B McQueen
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, VIC, Australia
| | - A Miljevic
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, VIC, Australia
| | - P B Fitzgerald
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, VIC, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Monarch Research Institute Monarch Mental Health Group, Sydney, NSW, Australia
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9
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Introducing RELAX: An automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and application to oscillations. Clin Neurophysiol 2023; 149:178-201. [PMID: 36822997 DOI: 10.1016/j.clinph.2023.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 01/23/2023] [Accepted: 01/27/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVE Electroencephalographic (EEG) data are often contaminated with non-neural artifacts which can confound experimental results. Current artifact cleaning approaches often require costly manual input. Our aim was to provide a fully automated EEG cleaning pipeline that addresses all artifact types and improves measurement of EEG outcomes METHODS: We developed RELAX (the Reduction of Electroencephalographic Artifacts). RELAX cleans continuous data using Multi-channel Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were compared using three datasets (N = 213, 60 and 23 respectively) against six commonly used pipelines across a range of artifact cleaning metrics, including measures of remaining blink and muscle activity, and the variance explained by experimental manipulations after cleaning. RESULTS RELAX with MWF and wICA_ICLabel showed amongst the best performance at cleaning blink and muscle artifacts while preserving neural signal. RELAX with wICA_ICLabel only may perform better at differentiating alpha oscillations between working memory conditions. CONCLUSIONS RELAX provides automated, objective and high-performing EEG cleaning, is easy to use, and freely available on GitHub. SIGNIFICANCE We recommend RELAX for data cleaning across EEG studies to reduce artifact confounds, improve outcome measurement and improve inter-study consistency.
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10
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Buzzell GA, Morales S, Valadez EA, Hunnius S, Fox NA. Maximizing the potential of EEG as a developmental neuroscience tool. Dev Cogn Neurosci 2023; 60:101201. [PMID: 36732112 PMCID: PMC10150174 DOI: 10.1016/j.dcn.2023.101201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Affiliation(s)
- George A Buzzell
- Department of Psychology, Florida International University, USA; Center for Children and Families, Florida International University, USA.
| | - Santiago Morales
- Department of Psychology, University of Southern California, USA
| | - Emilio A Valadez
- Department of Human Development and Quantitative Methodology, University of Maryland - College Park, USA
| | - Sabine Hunnius
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland - College Park, USA
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11
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Hernández-Álvarez L, Barbierato E, Caputo S, Mucchi L, Hernández Encinas L. EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers. SENSORS (BASEL, SWITZERLAND) 2022; 23:186. [PMID: 36616785 PMCID: PMC9823500 DOI: 10.3390/s23010186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/15/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user's inherent characteristics and, therefore, offers personalized authentication systems. Specifically, we propose an electrocardiogram (EEG)-based user authentication system by employing One-Class and Multi-Class Machine Learning classifiers. In this sense, the main novelty of this article is the introduction of Isolation Forest and Local Outlier Factor classifiers as new tools for user authentication and the investigation of their suitability with EEG data. Additionally, we identify the EEG channels and brainwaves with greater contribution to the authentication and compare them with the traditional dimensionality reduction techniques, Principal Component Analysis, and χ2 statistical test. In our final proposal, we elaborate on a hybrid system resistant to random forgery attacks using an Isolation Forest and a Random Forest classifiers, obtaining a final accuracy of 82.3%, a precision of 91.1% and a recall of 75.3%.
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Affiliation(s)
- Luis Hernández-Álvarez
- Computer Security Lab, Universidad Carlos III de Madrid, 28911 Leganés, Spain
- Institute of Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, Spain
| | - Elena Barbierato
- Department of Agriculture, Food, Environment and Forestry, University of Florence, 50144 Firenze, Italy
| | - Stefano Caputo
- Department of Information Engineering, University of Florence, 50139 Firenze, Italy
| | - Lorenzo Mucchi
- Department of Information Engineering, University of Florence, 50139 Firenze, Italy
| | - Luis Hernández Encinas
- Institute of Physical and Information Technologies, Spanish National Research Council, 28006 Madrid, Spain
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12
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Kumaravel VP, Buiatti M, Parise E, Farella E. Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF). SENSORS (BASEL, SWITZERLAND) 2022; 22:7314. [PMID: 36236413 PMCID: PMC9571252 DOI: 10.3390/s22197314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts' nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults.
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Affiliation(s)
- Velu Prabhakar Kumaravel
- Digital Society Center, Fondazione Bruno Kessler, 38123 Trento, Italy
- Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
| | - Marco Buiatti
- Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
| | - Eugenio Parise
- Center for Mind/Brain Sciences, University of Trento, 38068 Rovereto, Italy
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Zangeneh Soroush M, Tahvilian P, Nasirpour MH, Maghooli K, Sadeghniiat-Haghighi K, Vahid Harandi S, Abdollahi Z, Ghazizadeh A, Jafarnia Dabanloo N. EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms. Front Physiol 2022; 13:910368. [PMID: 36091378 PMCID: PMC9449652 DOI: 10.3389/fphys.2022.910368] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/11/2022] [Indexed: 11/22/2022] Open
Abstract
Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.
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Affiliation(s)
- Morteza Zangeneh Soroush
- Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran
- Bio-Intelligence Research Unit, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
- Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
- Department of Clinical Neuroscience, Mahdiyeh Clinic, Tehran, Iran
- *Correspondence: Morteza Zangeneh Soroush,
| | - Parisa Tahvilian
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
- Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad Hossein Nasirpour
- Department of Medical Genetics, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
- Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Khosro Sadeghniiat-Haghighi
- Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Sleep Breathing Disorders Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Sepide Vahid Harandi
- Department of Psychology, Islamic Azad University, Najafabad Branch, Najafabad, Iran
| | - Zeinab Abdollahi
- Department of Electrical and Computer Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran
| | - Ali Ghazizadeh
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran
- Bio-Intelligence Research Unit, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
- Engineering Research Center in Medicine and Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
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14
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Tamburro G, Jansen K, Lemmens K, Dereymaeker A, Naulaers G, De Vos M, Comani S. Automated detection and removal of flat line segments and large amplitude fluctuations in neonatal electroencephalography. PeerJ 2022; 10:e13734. [PMID: 35846889 PMCID: PMC9285485 DOI: 10.7717/peerj.13734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 06/24/2022] [Indexed: 01/17/2023] Open
Abstract
Background Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements). Method A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies. Results The algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application. Significance An automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications.
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Affiliation(s)
- Gabriella Tamburro
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy,BIND – Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Katrien Jansen
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium
| | - Katrien Lemmens
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium
| | | | - Gunnar Naulaers
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium
| | - Maarten De Vos
- Department of Development and Regeneration, UZ Leuven, Leuven, Belgium,Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Silvia Comani
- Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy,BIND – Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy
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Chen Y, Shi W, Liu Q, Chu H, Chen X, Yan L, Wu J, Li L, Gao X. EEG Measurement for Suppression in Refractive Amblyopia and Push-pull Perception Efficacy. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1321-1330. [PMID: 35576430 DOI: 10.1109/tnsre.2022.3175177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In order to evaluate refractive amblyopia suppression and understand the neural mechanism of amblyopia suppression and push-pull perception training, we recorded the EEG of refractive amblyopia children before, during, and after push-pull perception training. We compared the brain activity in different states through the steady-state visual evoked potentials (SSVEPs) response and power topography and compared them with normal children. We found that amblyopic and fellow eyes have different performances in fundamental and harmonic frequency responses. They also show different characteristics when be masked. Push-pull perception training improved the SSVEP performance of amblyopia children by reducing the SSVEP response difference between eyes and improving the intermodulation frequency response. The result of topography showed that push-pull perception reduced the alpha power of occipital and temporal lobes, which was conducive to improving binocular function. The changes of intermodulation response and occipital alpha power were significantly correlated with the clinical indicator. Thus, EEG is a potential method to measure amblyopia suppression and the efficacy of push-pull perception.
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