Browsing by Subject "Electroencephalography"

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  • Mutanen, Tuomas P.; Metsomaa, Johanna; Liljander, Sara; Ilmoniemi, Risto J. (2018)
    Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise-and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g., by identifying and rejecting the so-called "bad" channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor-or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas.
  • Tervo, Aino E.; Nieminen, Jaakko O.; Lioumis, Pantelis; Metsomaa, Johanna; Souza, Victor H.; Sinisalo, Heikki; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J. (2022)
    Background: Transcranial magnetic stimulation (TMS) is widely used in brain research and treatment of various brain dysfunctions. However, the optimal way to target stimulation and administer TMS therapies, for example, where and in which electric field direction the stimuli should be given, is yet to be determined. Objective: To develop an automated closed-loop system for adjusting TMS parameters (in this work, the stimulus orientation) online based on TMS-evoked brain activity measured with electroencephalography (EEG). Methods: We developed an automated closed-loop TMS-EEG set-up. In this set-up, the stimulus parameters are electronically adjusted with multi-locus TMS. As a proof of concept, we developed an algorithm that automatically optimizes the stimulation orientation based on single-trial EEG responses. We applied the algorithm to determine the electric field orientation that maximizes the amplitude of the TMS-EEG responses. The validation of the algorithm was performed with six healthy volunteers, repeating the search twenty times for each subject. Results: The validation demonstrated that the closed-loop control worked as desired despite the large variation in the single-trial EEG responses. We were often able to get close to the orientation that maximizes the EEG amplitude with only a few tens of pulses. Conclusion: Optimizing stimulation with EEG feedback in a closed-loop manner is feasible and enables effective coupling to brain activity. (C) 2022 The Author(s). Published by Elsevier Inc.
  • Smeds, Eero; Vanhatalo, Sampsa; Piitulainen, Harri; Bourguignon, Mathieu; Jousmaki, Veikko; Hari, Riitta (2017)
    Objective: Somatosensory evoked potentials have high prognostic value in neonatal intensive care, but their recording from infants is challenging. Here, we studied the possibility to elicit cortical responses in newborns by simple passive hand movements. Methods: We examined 13 newborns (postnatal age 1-46 days) during clinically indicated 19-channel electroencephalography (EEG) recordings in the neonatal intensive care unit; EEG indications included birth asphyxia and suspected epileptic seizures. The experimenter moved the infant's wrist or fingers at 1 or 2 Hz for 5-10 min, separately on both sides. We measured movement kinematics with an accelerometer attached to the infant's hand and computed coherence between the EEG and acceleration signals (corticokinematic coherence, CKC). Results: Statistically significant CKC (amplitude 0.020-0.511) with characteristic scalp topography was observed in all infants at twice the movement frequency. CKC was contralaterally dominant on the central scalp (median laterality index 0.48 for right-hand and -0.63 for left-hand movements). Conclusions: Passive movements elicit cortical responses that can be readily observed in clinical EEG recordings from newborns in the intensive-care environment. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd.
  • O'Toole, John M.; Boylan, Geraldine B.; Lloyd, Rhodri O.; Goulding, Robert M.; Vanhatalo, Sampsa; Stevenson, Nathan J. (2017)
    Aim: To develop a method that segments preterm EEG into bursts and inter-bursts by extracting and combining multiple EEG features. Methods: Two EEG experts annotated bursts in individual EEG channels for 36 preterm infants with gestational age <30 weeks. The feature set included spectral, amplitude, and frequency-weighted energy features. Using a consensus annotation, feature selection removed redundant features and a support vector machine combined features. Area under the receiver operator characteristic (AUC) and Cohen's kappa (K) evaluated performance within a cross-validation procedure. Results: The proposed channel-independent method improves AUC by 4-5% over existing methods (p <0.001, n = 36), with median (95% confidence interval) AUC of 0.989 (0.973-0.997) and sensitivity -specificity of 95.8-94.4%. Agreement rates between the detector and experts' annotations, K = 0.72 (0.36-0.83) and K = 0.65 (0.32-0.81), are comparable to inter-rater agreement, K = 0.60 (0.21-0.74). Conclusions: Automating the visual identification of bursts in preterm EEG is achievable with a high level of accuracy. Multiple features, combined using a data-driven approach, improves on existing single-feature methods. (C) 2017 The Authors. Published by Elsevier Ltd on behalf of IPEM.
  • James, F. M. K.; Cortez, M. A.; Monteith, G.; Jokinen, T. S.; Sanders, S.; Wielaender, F.; Fischer, A.; Lohi, H. (2017)
    Background: Poor agreement between observers on whether an unusual event is a seizure drives the need for a specific diagnostic tool provided by video-electroencephalography (video-EEG) in human pediatric epileptology. Objective: That successful classification of events would be positively associated with increasing EEG recording length and higher event frequency reported before video-EEG evaluation; that a novel wireless video-EEG technique would clarify whether unusual behavioral events were seizures in unsedated dogs. Animals: Eighty-one client-owned dogs of various breeds undergoing investigation of unusual behavioral events at 4 institutions. Methods: Retrospective case series: evaluation of wireless video-EEG recordings in unsedated dogs performed at 4 institutions. Results: Electroencephalography achieved/excluded diagnosis of epilepsy in 58 dogs (72%); 25 dogs confirmed with epileptic seizures based on ictal/interictal epileptiform discharges, and 33 dogs with no EEG abnormalities associated with their target events. As reported frequency of the target events decreased (annually, monthly, weekly, daily, hourly, minutes, seconds), EEG was less likely to achieve diagnosis (P <0.001). Every increase in event frequency increased the odds of achieving diagnosis by 2.315 (95% confidence interval: 1.36-4.34). EEG recording length (mean = 3.69 hours, range: 0.17-22.5) was not associated (P = 0.2) with the likelihood of achieving a diagnosis. Conclusions and Clinical Importance: Wireless video-EEG in unsedated dogs had a high success for diagnosis of unusual behavioral events. This technique offered a reliable clinical tool to investigate the epileptic origin of behavioral events in dogs.
  • Salo, Karita S.-T.; Mutanen, Tuomas P.; Vaalto, Selja M. I.; Ilmoniemi, Risto J. (2020)
    The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is commonly applied for studying the effective connectivity of neuronal circuits. The stimulation excites neurons, and the resulting TMS-evoked potentials (TEPs) are recorded with EEG. A serious obstacle in this method is the generation of large muscle artifacts from scalp muscles, especially when frontolateral and temporoparietal, such as speech, areas are stimulated. Here, TMS–EEG data were processed with the signal-space projection and source-informed reconstruction (SSP–SIR) artifact-removal methods to suppress these artifacts. SSP–SIR suppressed muscle artifacts according to the difference in frequency contents of neuronal signals and muscle activity. The effectiveness of SSP–SIR in rejecting muscle artifacts and the degree of excessive attenuation of brain EEG signals were investigated by comparing the processed versions of the recorded TMS–EEG data with simulated data. The calculated individual lead-field matrix describing how the brain signals spread on the cortex were used as simulated data. We conclude that SSP–SIR was effective in suppressing artifacts also when frontolateral and temporoparietal cortical sites were stimulated, but it may have suppressed also the brain signals near the stimulation site. Effective connectivity originating from the speech-related areas may be studied even when speech areas are stimulated at least on the contralateral hemisphere where the signals were not suppressed that much.
  • Shatilov, Kirill A.; Chatzopoulos, Dimitris; Lee, Lik-Hang; Hui, Pan (2021)
    Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduceto the areaof XR.
  • Borovac, Ana; Gudmundsson, Steinn; Thorvardsson, Gardar; Moghadam, Saeed M.; Nevalainen, Päivi; Stevenson, Nathan; Vanhatalo, Sampsa; Runarsson, Thomas P. (2022)
    Objective: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. Methods and procedures: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. Results: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. Conclusion: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid-Skene method when local detectors approach performance of a single detector trained on all available data. Clinical impact: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.
  • Nevalainen, Päivi; Marchi, Viviana; Metsäranta, Marjo; Lönnqvist, Tuula; Toiviainen-Salo, Sanna; Vanhatalo, Sampsa; Lauronen, Leena (2017)
    Objective: To evaluate the added value of somatosensory (SEPs) and visual evoked potentials (VEPs) recorded simultaneously with routine EEG in early outcome prediction of newborns with hypoxicischemic encephalopathy under modern intensive care. Methods: We simultaneously recorded multichannel EEG, median nerve SEPs, and flash VEPs during the first few postnatal days in 50 term newborns with hypoxic-ischemic encephalopathy. EEG background was scored into five grades and the worst two grades were considered to indicate poor cerebral recovery. Evoked potentials were classified as absent or present. Clinical outcome was determined from the medical records at a median age of 21 months. Unfavorable outcome included cerebral palsy, severe mental retardation, severe epilepsy, or death. Results: The accuracy of outcome prediction was 98% with SEPs compared to 90% with EEG. EEG alone always predicted unfavorable outcome when it was inactive (n = 9), and favorable outcome when it was normal or only mildly abnormal (n = 17). However, newborns with moderate or severe EEG background abnormality could have either favorable or unfavorable outcome, which was correctly predicted by SEP in all but one newborn (accuracy in this subgroup 96%). Absent VEPs were always associated with an inactive EEG, and an unfavorable outcome. However, presence of VEPs did not guarantee a favorable outcome. Conclusions: SEPs accurately predict clinical outcomes in newborns with hypoxic-ischemic encephalopathy and improve the EEG-based prediction particularly in those newborns with severely or moderately abnormal EEG findings. Significance: SEPs should be added to routine EEG recordings for early bedside assessment of newborns with hypoxic-ischemic encephalopathy. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
  • Kostilainen, Kaisamari; Wikstrom, Valtteri; Pakarinen, Satu; Videman, Mari; Karlsson, Linnea; Keskinen, Maria; Scheinin, Noora M.; Karlsson, Hasse; Huotilainen, Minna (2018)
    We evaluated the feasibility of a multi-feature mismatch negativity (MMN) paradigm in studying auditory processing of healthy newborns. The aim was to examine the automatic change-detection and processing of semantic and emotional information in speech in newborns. Brain responses of 202 healthy newborns were recorded with a multi-feature paradigm including a Finnish bi-syllabic pseudo-word/ta-ta/as a standard stimulus, six linguistically relevant deviant stimuli and three emotionally relevant stimuli (happy, sad, angry). Clear responses to emotional sounds were found already at the early latency window 100-200 ms, whereas responses to linguistically relevant minor changes and emotional stimuli at the later latency window 300-500 ms did not reach significance. Moreover, significant interaction between gender and emotional stimuli was found in the early latency window. Further studies on using multi-feature paradigms with linguistic and emotional stimuli in newborns are needed, especially those containing of follow-ups, enabling the assessment of the predictive value of early variations between subjects.
  • Kuula, Liisa; Tamminen, Jakke; Makkonen, Tommi; Merikanto, Ilona; Räikkönen, Katri; Pesonen, Anu-Katriina (2019)
    Background: Sleep facilitates the extraction of semantic regularities amongst newly encoded memories, which may also lead to increased false memories. We investigated sleep stage proportions and sleep spindles in the recollection of adolescents' false memories, and their potential sex-specific differences. Methods: 196 adolescents (mean age 16.9 y; SD = 0.1, 61% girls) underwent the Deese, Roediger & McDermott (DRM) false memory procedure and overnight polysomnography, with free recall the following morning. Sleep was scored manually into stages 1, 2, 3 and REM. Stage 2 sleep spindle frequency, density, and peak amplitude were used as measures of spindle activity for slow (10-13 Hz) and fast (13-16 Hz) ranges. Results: In girls, a lower number of critical lures was associated with higher spindle frequency (p Conclusions: In adolescent girls, higher spindle activity was associated with fewer critical lures being falsely recalled in the DRM paradigm. Unlike studies using adult participants, we did not observe any association between slow-wave sleep and false memory recollection.
  • Hari, Riitta; Baillet, Sylvain; Barnes, Gareth; Burgess, Richard; Forss, Nina; Gross, Joachim; Hämäläinen, Matti; Jensen, Ole; Kakigi, Ryusuke; Mauguière, François; Nakasato, Nobukatzu; Puce, Aina; Romani, Gian-Luca; Schnitzler, Alfons; Taulu, Samu (2018)
    Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.
  • Mutanen, Tuomas P.; Kukkonen, Matleena; Nieminen, Jaakko O.; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J. (2016)
    Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) often suffers from large muscle artifacts. Muscle artifacts can be removed using signal-space projection (SSP), but this canmake the visual interpretation of the remaining EEG data difficult. We suggest to use an additional step after SSP that we call source-informed reconstruction (SIR). SSP-SIR improves substantially the signal quality of artifactual TMS-EEG data, causing minimal distortion in the neuronal signal components. In the SSP-SIR approach, we first project out the muscle artifact using SSP. Utilizing an anatomical model and the remaining signal, we estimate an equivalent source distribution in the brain. Finally, we map the obtained source estimate onto the original signal space, again using anatomical information. This approach restores the neuronal signals in the sensor space and interpolates EEG traces onto the completely rejected channels. The introduced algorithm efficiently suppresses TMS-related muscle artifacts in EEG while retaining well the neuronal EEG topographies and signals. With the presented method, we can remove muscle artifacts from TMS-EEG data and recover the underlying brain responses without compromising the readability of the signals of interest. (C) 2016 Elsevier Inc. All rights reserved.
  • Guzman-Lopez, Jessica; Hernandez-Pavon, Julio C.; Lioumis, Pantelis; Mäkelä, Jyrki P.; Silvanto, Juha (2022)
    Objective: The impact of transcranial magnetic stimulation (TMS) has been shown to depend on the initial brain state of the stimulated cortical region. This observation has led to the development of paradigms that aim to enhance the specificity of TMS effects by using visual/luminance adaptation to modulate brain state prior to the application of TMS. However, the neural basis of interactions between TMS and adaptation is unknown. Here, we examined these interactions by using electroencephalography (EEG) to measure the impact of TMS over the visual cortex after luminance adaptation. Methods: Single-pulses of neuronavigated TMS (nTMS) were applied at two different intensities over the left visual cortex after adaptation to either high or low luminance. We then analyzed the effects of adaptation on the global and local cortical excitability. Results: The analysis revealed a significant interaction between the TMS-evoked responses and the adaptation condition. In particular, when nTMS was applied with high intensity, the evoked responses were larger after adaptation to high than low luminance.Conclusion: This result provides the first neural evidence on the interaction between TMS with visual adaptation. Significance: TMS can activate neurons differentially as a function of their adaptation state.(c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (
  • Tapani, Karoliina T.; Vanhatalo, Sampsa; Stevenson, Nathan J. (2019)
    The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-frequency domain (time-frequency correlation; TFC). These measures were incorporated into a seizure detection algorithm (SDA) based on a support vector machine to detect periods of seizure and nonseizure. The performance of these nonstationary correlation measures was evaluated using EEG recordings from 79 term neonates annotated by three human experts. The proposed measures were highly discriminative for seizure detection (median AUC(SC): 0.933 IQR: 0.821-0.975, median AUC(TFC): 0.883 IQR: 0.707-0.931). The resultant SDA applied to multi-channel recordings had a median AUC of 0.988 (IQR: 0.931-0.998) when compared to consensus annotations, outperformed two state-of-the-art SDAs (p <0.001) and was noninferior to the human expert for 73/79 of neonates.
  • Mäkelä , Niko; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J. (2018)
    Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto-or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications.
  • Iso-Markku, Paula; Waller, Katja; Hautasaari, Pekka; Kaprio, Jaakko; Kujala, Urho M.; Tarkka, Ina M. (2020)
    Regular physical activity (PA) offers positive effects on the human body. However, the effects of PA on cognition and in the brain are less clear. In this paper, we narratively review the relationship of PA with cognition and dementia, first from general perspective and then through genetically informed studies on the topic. Then we move on to imaging studies on exercise and brain anatomy first by presenting an overall picture of the topic and then discussing brain imaging studies addressing PA and brain structure in twins in more detailed way. Regarding PA and cognition or dementia, genetically informed studies are uncommon, even though the relationship between PA and cognitive ageing has been extensively studied. It is challenging to find twin pairs discordant for PA and dementia. Concerning brain imaging studies, among PA discordant young adult twin pairs, the more active co-twins showed larger gray matter volumes in striatal, prefrontal, and hippocampal regions and in electrophysiological studies automatic deviance-detection processes differed in brain regions involved with sensorimotor, visual and memory functions.
  • 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.
  • Laitinen, Juulia (Helsingin yliopisto, 2020)
    Status epilepticus on elimistön hätätila, johon liittyy merkittävä vammautumis- sekä kuolleisuusriski. Päivystyksellinen elektroenkefalografia (EEG) on nonkonvulsiivisen status epilepticuksen diagnosoinnin tärkeä työkalu. Status epilepticuksen nopea hoito parantaa potilaan ennustetta ja ehkäisee vakavia komplikaatioita. Tutkimus pyrkii selvittämään virka-ajan ulkopuolisten EEG-tutkimusten kysyntää, löydöksiä sekä osuvuutta nonkonvulsiivisen status epilepticuksen diagnosoinnissa. Tutkimus antaa Kliinisen neurofysiologian osastolle objektiivista tietoa nykyisten EEG-käytäntöjen toimivuudesta. Retrospektiivinen arkistotutkimus käsittelee potilaita, joille tehtiin 8-kanavainen myssy-EEG virka-ajan ulkopuolella vuosina 2018-2020 HUS Meilahden sairaalassa. Vuosien 2013-2016 potilaista on tehty pilottitutkimus, jonka tuloksiin vertaamme viime vuosien tilannetta. Myssy-EEG tehtiin kahden vuoden aikana virka-ajan ulkopuolella 160 potilaalle. Status epilepticuksia löytyi vain 6% rekisteröinneistä. Suurin osa löydöksistä oli normaaleja tai yleishäiriöitä. 26% potilaista tehtiin 21-kanavainen kontrolli-EEG-rekisteröinti, jonka tulos vastasi varsin hyvin myssy-EEG:tä. Elektrodipaikkojen muutos keväällä 2019 ei lisännyt status epilepticus-löydöksiä. Tässä tutkimuksessa ei saatu viitteitä siitä, että myssy-EEG olisi yhdessäkään tapauksessa jättänyt SE:n tunnistamatta. Öisin rekisteröityjä myssy-EEG:itä tarkasteltiin vuosina 2016-2020. Tuona aikana rekisteröinti tehtiin 31 potilaalle. Status epilepticuksia esiintyi öisinkin 6 prosentissa rekisteröinneistä. Neurologit osasivat poissulkea status epilepticuksen varsin hyvin, mutta kolmasosa potilaista hoidettiin silti kliinisen oireilun perusteella. Yksikään SE ei jäänyt vaille hoitoa. Sekä päivystys-EEG:t että status epilepticukset painottuvat selvästi virka-aikaan. Virka-ajan ulkopuolisten EEG-rekisteröintien määrä on kasvanut viime vuosina, mutta status epilepticuksien määrä on pysynyt samana. Myssy-EEG tunnistaa status epilepticuksen riittävän tarkasti. Nykyinen EEG-käytäntö on toimiva ja luotettava status epilepticuksen diagnostiikan osalta.
  • Törnqvist, Heini; Kujala, Miiamaaria V.; Somppi, Sanni; Hänninen, Laura; Pastell, Matti; Krause, Christina M.; Kujala, Jan; Vainio, Outi (2013)
    Previously, social and cognitive abilities of dogs have been studied within behavioral experiments, but the neural processing underlying the cognitive events remains to be clarified. Here, we employed completely non-invasive scalp-electroencephalography in studying the neural correlates of the visual cognition of dogs. We measured visual event-related potentials (ERPs) of eight dogs while they observed images of dog and human faces presented on a computer screen. The dogs were trained to lie still with positive operant conditioning, and they were neither mechanically restrained nor sedated during the measurements. The ERPs corresponding to early visual processing of dogs were detectable at 75–100 ms from the stimulus onset in individual dogs, and the group-level data of the 8 dogs differed significantly from zero bilaterally at around 75 ms at the most posterior sensors. Additionally, we detected differences between the responses to human and dog faces in the posterior sensors at 75–100 ms and in the anterior sensors at 350–400 ms. To our knowledge, this is the first illustration of completely non-invasively mea- sured visual brain responses both in individual dogs and within a group-level study, using ecologically valid visual stimuli. The results of the present study validate the fea- sibility of non-invasive ERP measurements in studies with dogs, and the study is expected to pave the way for further neurocognitive studies in dogs.