Browsing by Subject "electroencephalography"

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  • Moura, Fernando S.; Beraldo, Roberto G.; Ferreira, Leonardo A.; Siltanen, Samuli (2021)
    Objective. The objective of this work is to develop a 4D (3D+T) statistical anatomical atlas of the electrical properties of the upper part of the human head for cerebral electrophysiology and bioimpedance applications. Approach. The atlas was constructed based on 3D magnetic resonance images (MRI) of 107 human individuals and comprises the electrical properties of the main internal structures and can be adjusted for specific electrical frequencies. T1w+T2w MRI images were used to segment the main structures of the head while angiography MRI was used to segment the main arteries. The proposed atlas also comprises a time-varying model of arterial brain circulation, based on the solution of the Navier-Stokes equation in the main arteries and their vascular territories. Main results. High-resolution, multi-frequency and time-varying anatomical atlases of resistivity, conductivity and relative permittivity were created and evaluated using a forward problem solver for EIT. The atlas was successfully used to simulate electrical impedance tomography measurements indicating the necessity of signal-to-noise between 100 and 125 dB to identify vascular changes due to the cardiac cycle, corroborating previous studies. The source code of the atlas and solver are freely available to download. Significance. Volume conductor problems in cerebral electrophysiology and bioimpedance do not have analytical solutions for nontrivial geometries and require a 3D model of the head and its electrical properties for solving the associated PDEs numerically. Ideally, the model should be made with patient-specific information. In clinical practice, this is not always the case and an average head model is often used. Also, the electrical properties of the tissues might not be completely known due to natural variability. Anatomical atlases are important tools for in silico studies on cerebral circulation and electrophysiology that require statistically consistent data, e.g. machine learning, sensitivity analyses, and as a benchmark to test inverse problem solvers.
  • Cowley, Benjamin U.; Korpela, Jussi (2018)
    Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: (i) batch processing that is easy for experts and novices alike; (ii) testing and comparison of preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAP's support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAP's capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap.
  • Sirola, Roosa (Helsingfors universitet, 2013)
    Visual working memory (VWM) maintains information for future usage. Several studies show that the cortical oscillations in the γ-frequency band (from 30 to 120 Hz) are modulated by the VWM performance. However, less is known about the cortical sources underlying the modulation of these oscillations in VWM. To address this question, we recorded human neuronal activity with magneto- and electroencephalography (M/EEG) during a delayed-matching-to-sample VWM task with three different task conditions, within which participants were instructed to focus on different object features in turn. In addition, anatomical data was acquired with magnetic resonance imaging for source modeling purposes. We then estimated the cortical amplitude dynamics across frequencies from three to 90 Hz during the VWM retention period for these three different conditions. We found that the amplitudes of the γ –frequency band oscillations were strengthened in the occipito-temporal cortical areas during the VWM for shapes but not for color or spatial locations. These data suggest that γ –band oscillations are fundamental in VWM, especially for visual stimuli requiring perceptual feature binding. Furthermore, cortical γ –band oscillations were found to be load dependently strengthened in the frontal cortex, where the central executive and attention associated processes are believed to take place. These data support the previous hypotheses stating that γ –band oscillations contribute to the maintenance of object representations in VWM.
  • Leino, Akseli; Korkalainen, Henri; Kalevo, Laura; Nikkonen, Sami; Kainulainen, Samu; Ryan, Alexander; Duce, Brett; Sipila, Kirsi; Ahlberg, Jari; Sahlman, Johanna; Miettinen, Tomi; Westeren-Punnonen, Susanna; Mervaala, Esa; Toyras, Juha; Myllymaa, Sami; Leppanen, Timo; Myllymaa, Katja (2022)
    We have previously developed an ambulatory electrode set (AES) for the measurement of electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). The AES has been proven to be suitable for manual sleep staging and self-application in in-home polysomnography (PSG). To further facilitate the diagnostics of various sleep disorders, this study aimed to utilize a deep learning-based automated sleep staging approach for EEG signals acquired with the AES. The present neural network architecture comprises a combination of convolutional and recurrent neural networks previously shown to achieve excellent sleep scoring accuracy with a single standard EEG channel (F4-M1). In this study, the model was re-trained and tested with 135 EEG signals recorded with AES. The recordings were conducted for subjects suspected of sleep apnea or sleep bruxism. The performance of the deep learning model was evaluated with 10-fold cross-validation using manual scoring of the AES signals as a reference. The accuracy of the neural network sleep staging was 79.7% (kappa = 0.729) for five sleep stages (W, N1, N2, N3, and R), 84.1% (kappa = 0.773) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% (kappa = 0.801) for three sleep stages (W, NREM, R). The utilized neural network was able to accurately determine sleep stages based on EEG channels measured with the AES. The accuracy is comparable to the inter-scorer agreement of standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in-home PSGs.
  • Kalevo, Laura; Miettinen, Tomi; Leino, Akseli; Kainulainen, Samu; Korkalainen, Henri; Myllymaa, Katja; Töyräs, Juha; Leppänen, Timo; Laitinen, Tiina; Myllymaa, Sami (2020)
    In response to the growing clinico-economic need for comprehensive home-based sleep testing, we recently developed a self-applicable facial electrode set with screen-printed Ag/AgCl electrodes. Our previous studies revealed that nocturnal sweating is a common problem, causing low-frequency artifacts in the measured electroencephalography (EEG) signals. As the electrode set is designed to be used without skin abrasion, not surprisingly this leads to relatively high electrode-skin impedances, significant impedance changes due to sweating and an increased risk of sweat artifacts. However, our recent electrochemical in vitro investigations revealed that the sweat artifact tolerance of an EEG electrode can be improved by utilizing an appropriate Ag/AgCl ink. Here we have investigated in vivo electrode-skin impedances and the quality of EEG signals and interference due to sweating in the population of 11 healthy volunteers. Commercial Ag and Ag/AgCl inks (Engineered Conductive Materials ECM LLC and PPG Industries Inc.) were used to test electrode sets with differently constructed ink layers. Electrode-skin impedances and EEG signals were recorded before and after exercise-induced sweating. There was extensive variation in the electrode-skin impedances between the volunteers and the electrode positions: 14.6 & x2013;200 (PPG electrodes) and 7.7 & x2013;200 (ECM electrodes). Sweating significantly decreased the impedances in most cases. The EEG signal quality was assessed by comparing average band powers from 0.5 to 2 Hz before and after sweating. Only slight differences existed between the ECM and PPG electrodes; however, the lowest band power ratio (i.e. the smallest increase in the band power due to sweating) was achieved with ECM electrodes.
  • Cowley, Benjamin; Kirjanen, Svetlana; Partanen, Juhani; Castrén, Maija (2016)
    Fragile X syndrome (FXS) is the most common cause of inherited intellectual disability and a variant of autism spectrum disorder (ASD). The FXS population is quite heterogeneous with respect to comorbidities, which implies the need for a personalized medicine approach, relying on biomarkers or endophenotypes to guide treatment. There is evidence that quantitative electroencephalography (EEG) endophenotype-guided treatments can support increased clinical benefit by considering the patient's neurophysiological profile. We describe a case series of 11 children diagnosed with FXS, aged one to 14 years, mean 4.6 years. Case data are based on longitudinal clinically-observed reports by attending physicians for comorbid symptoms including awake and asleep EEG profiles. We tabulate the comorbid EEG symptoms in this case series, and relate them to the literature on EEG endophenotypes and associated treatment options. The two most common endophenotypes in the data were diffuse slow oscillations and epileptiform EEG, which have been associated with attention and epilepsy respectively. This observation agrees with reported prevalence of comorbid behavioral symptoms for FXS. In this sample of FXS children, attention problems were found in 37% (4 of 11), and epileptic seizures in 45% (5 of 11). Attention problems were found to associate with the epilepsy endophenotype. From the synthesis of this case series and literature review, we argue that the evidence-based personalized treatment approach, exemplified by neurofeedback, could benefit FXS children by focusing on observable, specific characteristics of comorbid disease symptoms.
  • Salminen, Mikko; Järvelä, Simo; Ruonala, Antti; Harjunen, Ville Johannes; Jacucci, Giulio; Hamari, Juho; Ravaja, Niklas (2022)
    With the advent of consumer grade virtual reality (VR) headsets and physiological measurement devices, new possibilities for mediated social interaction emerge enabling the immersion to environments where the visual features react to the users' physiological activation. In this study, we investigated whether and how individual and interpersonally shared biofeedback (visualised respiration rate and frontal asymmetry of electroencephalography, EEG) enhance synchrony between the users' physiological activity and perceived empathy towards the other during a compassion meditation exercise carried out in a social VR setting. The study was conducted as a laboratory experiment (N = 72) employing a Unity3D-based Dynecom immersive social meditation environment and two amplifiers to collect the psychophysiological signals for the biofeedback. The biofeedback on empathy-related EEG frontal asymmetry evoked higher self-reported empathy towards the other user than the biofeedback on respiratory activation, but the perceived empathy was highest when both feedbacks were simultaneously presented. In addition, the participants reported more empathy when there was stronger EEG frontal asymmetry synchronization between the users. The presented results inform the field of affective computing on the possibilities that VR offers for different applications of empathic technologies.
  • Alho, Kimmo; Zarnowiec, Katarzyna; Gorina-Careta, Natalia; Escera, Carles (2019)
    In electroencephalography (EEG) measurements, processing of periodic sounds in the ascending auditory pathway generates the frequency-following response (FFR) phase-locked to the fundamental frequency (F0) and its harmonics of a sound. We measured FFRs to the steady-state (vowel) part of syllables /ba/ and /aw/ occurring in binaural rapid streams of speech sounds as frequently repeating standard syllables or as infrequent (p = 0.2) deviant syllables among standard /wa/ syllables. Our aim was to study whether concurrent active phonological processing affects early processing of irrelevant speech sounds reflected by FFRs to these sounds. To this end, during syllable delivery, our healthy adult participants performed tasks involving written letters delivered on a computer screen in a rapid stream. The stream consisted of vowel letters written in red, infrequently occurring consonant letters written in the same color, and infrequently occurring vowel letters written in blue. In the phonological task, the participants were instructed to press a response key to the consonant letters differing phonologically but not in color from the frequently occurring red vowels, whereas in the non-phonological task, they were instructed to respond to the vowel letters written in blue differing only in color from the frequently occurring red vowels. We observed that the phonological task enhanced responses to deviant /ba/ syllables but not responses to deviant /aw/ syllables. This suggests that active phonological task performance may enhance processing of such small changes in irrelevant speech sounds as the 30-ms difference in the initial formant-transition time between the otherwise identical syllables /ba/ and /wa/ used in the present study.
  • Issakainen, Jani (Helsingin yliopisto, 2021)
    Electroencephalography (EEG) is a non-invasive neurophysiological method for evaluating brain activity by measuring electrical potential at the scalp. The electrical potentials originate mainly from postsynaptic cortical currents created by neuronal activity. It is a valuable tool for both research and clinical practice. EEG can be used e.g. to diagnose epilepsy, focal brain disorders, brain death, and coma. Intermittent photic stimulation (IPS) is an important tool in clinical EEG. Healthcare professionals use it to induce epileptic activity in patients to help diagnose their conditions. In these tests, various IPS frequencies are used with eyes-closed, eyes-open, and eye-closure conditions. IPS test is listed in clinical practice guidelines in EEG globally, and it is mainly used to diagnose photosensitive epilepsy, i.e., to detect epilepsy-related abnormal sensitivity to flickering light. Magnetoencephalography (MEG) is a non-invasive neurophysiological method in which minute magnetic fields — produced by the same postsynaptic currents as in EEG — are measured with special superconductive sensors around the head. MEG is a valuable tool for research and clinical practice with increasing world-wide utilization. The main advantages of MEG over EEG are easier source modelling and higher resolution at cortical areas. IPS has not been introduced to MEG since the IPS stimulators used in EEG are not compatible with MEG. IPS in MEG could improve the analysis of IPS and provide better tools for diagnoses. Currently, data analysis of IPS is typically limited to healthcare professionals examining the visualization of the raw data while looking for induced epileptiform activites and lateralizing them. In this thesis, an MEG-compatible IPS stimulator is introduced and alternative ways of analyzing IPS data for both MEG and EEG are showcased. Although analysis methods were applied with decent signal-to-noise ratios, further research is needed—especially to compare responses between patients with epilepsy and healthy subjects.
  • Ukai, Masayasu; Parmentier, Thomas; Cortez, Miguel A.; Fischer, Andrea; Gaitero, Luis; Lohi, Hannes; Nykamp, Stephanie; Jokinen, Tarja S.; Powers, Danielle; Sammut, Veronique; Sanders, Sean; Tai, Tricia; Wielaender, Franziska; James, Fiona (2021)
    Background Many studies of epilepsy in veterinary medicine use subjective data (eg, caregiver-derived histories) to determine seizure frequency. Conversely, in people, objective data from electroencephalography (EEG) are mainly used to diagnose epilepsy, measure seizure frequency and evaluate efficacy of antiseizure drugs. These EEG data minimize the possibility of the underreporting of seizures, a known phenomenon in human epileptology. Objective To evaluate the correlation between reported seizure frequency and EEG frequency of ictal paroxysmal discharges (PDs) and to determine whether seizure underreporting phenomenon exists in veterinary epileptology. Animals Thirty-three ambulatory video-EEG recordings in dogs showing >= 1 ictal PD, excluding dogs with status epilepticus. Methods Retrospective observational study. Ictal PDs were counted manually over the entire recording to obtain the frequency of EEG seizures. Caregiver-reported seizure frequency from the medical record was categorized into weekly, daily, hourly, and per minute seizure groupings. The Spearman rank test was used for correlation analysis. Results The coefficient value (r(s)) comparing reported seizure to EEG-confirmed ictal PD frequencies was 0.39 (95% confidence interval [CI] = 0.048-0.64, P = .03). Other r(s) values comparing history against various seizure types were: 0.36 for motor seizures and 0.37 for nonmotor (absence) seizures. Conclusions and Clinical Importance A weak correlation was found between the frequency of reported seizures from caregivers (subjective data) and ictal PDs on EEG (objective data). Subjective data may not be reliable enough to determine true seizure frequency given the discrepancy with EEG-confirmed seizure frequency. Confirmation of the seizure underreporting phenomenon in dogs by prospective study should be carried out.
  • Kalevo, Laura; Miettinen, Tomi; Leino, Akseli; Westeren-Punnonen, Susanna; Sahlman, Johanna; Mervaala, Esa; Toyras, Juha; Leppanen, Timo; Myllymaa, Sami; Myllymaa, Katja (2022)
    Home sleep apnea testing (HSAT) without electroencephalography (EEG) recording is increasingly used as an alternative to in-laboratory polysomnography for the diagnosis of obstructive sleep apnea (OSA). However, without EEG, electrooculography (EOG), and chin electromyography (EMG) recordings, the OSA severity may be significantly underestimated. Although several ambulatory EEG systems have been recently introduced, no patient-applied systems including EEG, EOG, and chin EMG suitable for home polysomnography are currently in clinical use. We have recently developed and pre-clinically tested a self-applied ambulatory electrode set (AES), consisting of frontal EEG, EOG, and EMG, in subjects with possible sleep bruxism. Now, in this clinical feasibility study, we investigated the signal scorability and usability of the AES as a self-administered sleep assessment approach supplementing the conventional HSAT device. We also investigated how the diagnostic parameters and OSA severity changed when utilizing the AES. Thirty-eight patients (61 % male, 25-78 years) with a clinical suspicion of OSA conducted a single-night, self-administered HSAT with a portable polysomnography device (Nox A1, Nox Medical, Reykjavik, Iceland) supplemented with AES. Only one AES recording failed. The use of AES signals in data analysis significantly affected the median apnea-hypopnea index (AHI), increasing it from 9.4 to 12.7 events/h (p < 0.001) compared to the conventional HSAT. Also, in eight patients, the OSA severity class changed to one class worse. Perceived ease of use was well in line with that previously found among healthy volunteers. These results suggest that the AES provides an easy, clinically feasible solution to record EEG as a part of conventional HSAT.
  • Banville, Hubert; Albuquerque, Isabela; Hyvärinen, Aapo; Moffat, Grame; Engemann, Denis-Alexander; Gramfort, Alexandre (IEEE, 2019)
  • Miettinen, Tomi; Myllymaa, Katja; Westeren-Punnonen, Susanna; Ahlberg, Jari; Hukkanen, Taina; Töyräs, Juha; Lappalainen, Reijo; Mervaala, Esa; Sipilä, Kirsi; Myllymaa, Sami (2018)
    Using sleep laboratory polysomnography (PSG) is restricted for the diagnosis of only the most severe sleep disorders due to its low availability and high cost. Home PSG is more affordable, but applying conventional electroencephalography (EEG) electrodes increases its overall complexity and lowers the availability. Simple, self-administered single-channel EEG monitors on the other hand suffer from poor reliability. In this study, we aimed to quantify the reliability of self-administrated home PSG recordings conducted with a newly designed ambulatory electrode set (AES) that enables multichannel EEG, electrooculography, electromyography, and electrocardiography recordings. We assessed the sleep study success rate and technical quality of the recordings performed in subjects with possible sleep bruxism (SB). Thirty-two females and five males aged 39.6 +/- 11.6 years (mean +/- SD) with self-reported SB were recruited in the study. Self-administrated home PSG recordings with two AES designs were conducted (n = 19 and 21). The technical quality of the recordings was graded based on the proportion of interpretable data. Technical failure rate for AES (both designs) was 5% and SB was scorable for 96.9% of all recorded data. Only one recording failed due to mistakes in self-applying the AES. We found that the proportion of good quality self-administrated EEG recordings is significantly higher when multiple channels are used compared to using a single channel. Sleep study success rates and proportion of recordings with high quality interpretable data from EEG channels of AES were comparable to that of conventional home PSG. Self-applicable AES has potential to become a reliable tool for widely available home PSG.
  • Mannermaa, Kristiina (Helsingin yliopisto, 2017)
    Previous research has linked music training to enhanced processing of unattended auditory stimuli as indexed by such auditory event-related potential (ERP) responses as mismatch negativity (MMN) and P3a. Music training has also been linked with enhanced cognitive abilities more generally, and executive functions have been proposed to mediate this link. The current study concentrates on the processing of unattended auditory stimuli and how this relates to two aspects of executive functions: task-switching and inhibition. Sixty-seven music trained (music group) and non-trained (control group) adolescents and young adults were split into age groups, 14–16 year olds (younger) and 17–20 year olds (older), and compared in their performance on inhibition and task-switching task as well as the neural processing of unattended auditory stimuli. The ERPs were recorded in response to an oddball paradigm consisting of frequent major and infrequent minor chords. The music group demonstrated larger MMN and P3a amplitudes than the control group during the chord paradigm. The younger music group showed better performance in an inhibition task than the younger control group. However, no other differences in task performance were found between the groups. Also, no link between MMN or P3a and task performance was found. Therefore, the results of the current study are in line with the previous findings that music training is linked to enhanced early neural processing of unattended auditory stimuli. However, the results were partly in disagreement with previous reports of enhanced executive functions in musicians as a link between executive functions and music training was only observed in the younger participants, and only in regard to the inhibition task.
  • Halonen, Risto (Helsingfors universitet, 2017)
    Sleep spindles are thalamocortical oscillations that occupy the sigma band with trait-like inter-individual variability. Sleep spindles associate with reasoning abilities according to several studies, but some discrepancy exists in the strength and even direction of the associations. This may, to some extent, be due to methodological differences. The stage of brain maturation also affects spindle manifestation. In this community-based study, associations between spindle characteristics and reasoning abilities are examined in an understudied age group, adolescents. An all-night polysomnography was conducted at homes of 178 adolescents (104 girls). Working memory, visuospatial reasoning and verbal reasoning were measured in the same evening. An automatic algorithm was used to detect slow (10–13 Hz) and fast (13–16 Hz) spindles in frontal and central scalp derivations in NREM 2 sleep stage. The associations between spindle variables (density and intensity) and the cognitive test scores were analyzed with linear regression. Genders apart, the analyses were conducted first on the whole group and then separately on the Above Median (AM) and Below Median (BM) intelligence subgroups. In the analyses with all subjects, higher central fast density associated with better verbal reasoning in girls. When examining the subgroups separately, this association was not perceived in the AM group but appeared prominently in the BM group girls. No other associations were found between the spindle variables and the cognitive test scores. A positive spindle-intelligence relation is an established finding in females, but more commonly the association is typified by fluid/visuospatial reasoning and frontal brain areas. In the present study, young age may have related to the accentuated relative significance of more caudal brain regions and verbal intelligence in relation to spindles. The ongoing neural maturation and the heterogeneity of the sample may have contributed to the nature of the findings. More adolescent studies are needed to gain understanding of the matter.
  • Failla, Alberto; Filatovaite, Lauryna; Wang, Xiaowan; Vanhatalo, Sampsa; Dudink, Jeroen; de Vries, Linda S.; Benders, Manon; Stevenson, Nathan; Tataranno, Maria Lusia (2022)
    The primary aim of this study is to examine whether bursting interhemispheric synchrony (bIHS) in the first week of life of infants born extremely preterm, is associated with microstructural development of the corpus callosum (CC) on term equivalent age magnetic resonance imaging scans. The secondary aim is to address the effects of analgesics such as morphine, on bIHS in extremely preterm infants. A total of 25 extremely preterm infants (gestational age [GA] < 28 weeks) were monitored with the continuous two-channel EEG during the first 72 h and after 1 week from birth. bIHS was analyzed using the activation synchrony index (ASI) algorithm. Microstructural development of the CC was assessed at ~ 30 and ~ 40 weeks of postmenstrual age (PMA) using fractional anisotropy (FA) measurements. Multivariable regression analyses were used to assess the primary and secondary aim. Analyses were adjusted for important clinical confounders: morphine, birth weight z-score, and white matter injury score. Due to the reduced sample size, only the most relevant variables, according to literature, were included. ASI was not significantly associated with FA of the CC at 30 weeks PMA and at 40 weeks PMA (p >.5). ASI was positively associated with the administration of morphine (p <.05). Early cortical synchrony may be affected by morphine and is not associated with the microstructural development of the CC. More studies are needed to evaluate the long-term effects of neonatal morphine treatment to optimize sedation in this high-risk population.
  • Banville, Hubert; Chehab, Omar; Hyvarinen, Aapo; Engemann, Denis-Alexander; Gramfort, Alexandre (2021)
    Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels. Approach. We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically, we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection. We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches. Main results. Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects. Significance. We demonstrate the benefit of SSL approaches on EEG data. Our results suggest that self-supervision may pave the way to a wider use of deep learning models on EEG data.
  • Smalle, Eleonore H.M.; Daikoku, Tatsuya; Szmalec, Arnaud; Duyck, Wouter; Möttönen, Riikka (2022)
    Human learning is supported by multiple neural mechanisms that maturate at different rates and interact in mostly cooperative but also sometimes competitive ways. We tested the hypothesis that mature cognitive mechanisms constrain implicit statistical learning mechanisms that contribute to early language acquisition. Specifically, we tested the prediction that depleting cognitive control mechanisms in adults enhances their implicit, auditory word-segmentation abilities. Young adults were exposed to continuous streams of syllables that repeated into hidden novel words while watching a silent film. Afterward, learning was measured in a forced-choice test that contrasted hidden words with nonwords. The participants also had to indicate whether they explicitly recalled the word or not in order to dissociate explicit versus implicit knowledge. We additionally measured electroencephalography during exposure to measure neural entrainment to the repeating words. Engagement of the cognitive mechanisms was manipulated by using two methods. In experiment 1 (n = 36), inhibitory theta-burst stimulation (TBS) was applied to the left dorsolateral prefrontal cortex or to a control region. In experiment 2 (n = 60), participants performed a dual working-memory task that induced high or low levels of cognitive fatigue. In both experiments, cognitive depletion enhanced word recognition, especially when participants reported low confidence in remembering the words (i.e., when their knowledge was implicit). TBS additionally modulated neural entrainment to the words and syllables. These findings suggest that cognitive depletion improves the acquisition of linguistic knowledge in adults by unlocking implicit statistical learning mechanisms and support the hypothesis that adult language learning is antagonized by higher cognitive mechanisms.