It’s all about time : Precision and accuracy of Emotiv event-marking for ERPD research

Background The use of consumer-grade electroencephalography (EEG) systems for research purposes has become more prevalent. In event-related potential (ERP) research, it is critical that these systems have precise and accurate timing. The aim of the current study was to investigate the timing reliability of event-marking solutions used with Emotiv commercial EEG systems. Method We conducted three experiments. In Experiment 1 we established a jitter threshold (i.e. the point at which jitter made an event-marking method unreliable). To do this, we introduced statistical noise to the temporal position of event-marks of a pre-existing ERP dataset (recorded with a research-grade system, Neuroscan SynAmps2 at 1,000 Hz using parallel-port event-marking) and calculated the level at which the waveform peaks differed statistically from the original waveform. In Experiment 2 we established a method to identify ‘true’ events (i.e. when an event should appear in the EEG data). We did this by inserting 1,000 events into Neuroscan data using a custom-built event-marking system, the ‘Airmarker’, which marks events by triggering voltage spikes in two EEG channels. We used the lag between Airmarker events and events generated by Neuroscan as a reference for comparisons in Experiment 3. In Experiment 3 we measured the precision and accuracy of three types of Emotiv event-marking by generating 1,000 events, 1 s apart. We measured precision as the variability (standard deviation in ms) of Emotiv events and accuracy as the mean difference between Emotiv events and true events. The three triggering methods we tested were: (1) Parallel-port-generated TTL triggers; (2) Arduino-generated TTL triggers; and (3) Serial-port triggers. In Methods 1 and 2 we used an auxiliary device, Emotiv Extender, to incorporate triggers into the EEG data. We tested these event-marking methods across three configurations of Emotiv EEG systems: (1) Emotiv EPOC+ sampling at 128 Hz; (2) Emotiv EPOC+ sampling at 256 Hz; and (3) Emotiv EPOC Flex sampling at 128 Hz. Results In Experiment 1 we found that the smaller P1 and N1 peaks were attenuated at lower levels of jitter relative to the larger P2 peak (21 ms, 16 ms, and 45 ms for P1, N1, and P2, respectively). In Experiment 2, we found an average lag of 30.96 ms for Airmarker events relative to Neuroscan events. In Experiment 3, we found some lag in all configurations. However, all configurations exhibited precision of less than a single sample, with serial-port-marking the most precise when paired with EPOC+ sampling at 256 Hz. Conclusion All Emotiv event-marking methods and configurations that we tested were precise enough for ERP research as the precision of each method would provide ERP waveforms statistically equivalent to a research-standard system. Though all systems exhibited some level of inaccuracy, researchers could easily account for these during data processing

Case study: auditory brain responses in a minimally verbal child with autism and cerebral palsy

An estimated 30% of individuals with autism spectrum disorders (ASD) remain minimally verbal into late childhood, but research on cognition and brain function in ASD focuses almost exclusively on those with good or only moderately impaired language. Here we present a case study investigating auditory processing of GM, a nonverbal child with ASD and cerebral palsy. At the age of 8 years, GM was tested using magnetoencephalography (MEG) whilst passively listening to speech sounds and complex tones. Where typically developing children and verbal autistic children all demonstrated similar brain responses to speech and nonspeech sounds, GM produced much stronger responses to nonspeech than speech, particularly in the 65–165 ms (M50/M100) time window post-stimulus onset. GM was retested aged 10 years using electroencephalography (EEG) whilst passively listening to pure tone stimuli. Consistent with her MEG response to complex tones, GM showed an unusually early and strong response to pure tones in her EEG responses. The consistency of the MEG and EEG data in this single case study demonstrate both the potential and the feasibility of these methods in the study of minimally verbal children with ASD. Further research is required to determine whether GM's atypical auditory responses are characteristic of other minimally verbal children with ASD or of other individuals with cerebral palsy

Are different reading problems associated with different anxiety types?

There is a reliable association between poor reading and anxiety, but we do not completely understand the specifics of this relationship. The current study included a sample of children (N = 284; Mage = 9.30, SDage = 1.31) who completed a reading (word, nonword, and text reading accuracy, word, nonword, and text reading fluency, passage reading comprehension) and anxiety (social anxiety, generalized anxiety, separation anxiety, physical injury fears/phobias, panic, obsessive compulsive symptoms) assessment. Analyses included bivariate and partial correlations, principal components analysis, and hierarchical clustering. We found a very tentative suggestion in the data that there may be a specific yet weak association between reading accuracy and social anxiety. The clinical implications and directions for future research are discussed

The effect of non-communicative eye movements on joint attention.

Eye movements provide important signals for joint attention. However, those eye movements that indicate bids for joint attention often occur among non-communicative eye movements. This study investigated the influence of these non-communicative eye movements on subsequent joint attention responsivity. Participants played an interactive game with an avatar which required both players to search for a visual target on a screen. The player who discovered the target used their eyes to initiate joint attention. We compared participants’ saccadic reaction times (SRTs) to the avatar’s joint attention bids when they were preceded by non-communicative eye movements that predicted the location of the target (Predictive Search), did not predict the location of the target (Random Search), and when there were no non-communicative eye gaze movements prior to joint attention (No Search). We also included a control condition in which participants completed the same task, but responded to a dynamic arrow stimulus instead of the avatar’s eye movements. For both eye and arrow conditions, participants had slower SRTs in Random Search trials than No Search and Predictive Search trials. However, these effects were smaller for eyes than for arrows. These data suggest that joint attention responsivity for eyes is relatively stable to the presence and predictability of spatial information conveyed by non-communicative gaze. Contrastingly, random sequences of dynamic arrows had a much more disruptive impact on subsequent responsivity compared with predictive arrow sequences. This may reflect specialised social mechanisms and expertise for selectively responding to communicative eye gaze cues during dynamic interactions, which is likely facilitated by the integration of ostensive eye contact cue

A validation of Emotiv EPOC Flex saline for EEG and ERP research

Background Previous work has validated consumer-grade electroencephalography (EEG) systems for use in research. Systems in this class are cost-effective and easy to set up and can facilitate neuroscience outside of the laboratory. The aim of the current study was to determine if a new consumer-grade system, the Emotiv EPOC Saline Flex, was capable of capturing research-quality data. Method The Emotiv system was used simultaneously with a research-grade EEG system, Neuroscan Synamps2, to collect EEG data across 16 channels during five well-established paradigms: (1) a mismatch negativity (MMN) paradigm that involved a passive listening task in which rare deviant (1,500 Hz) tones were interspersed amongst frequent standard tones (1,000 Hz), with instructions to ignore the tones while watching a silent movie; (2) a P300 paradigm that involved an active listening task in which participants were asked to count rare deviant tones presented amongst frequent standard tones; (3) an N170 paradigm in which participants were shown images of faces and watches and asked to indicate whether the images were upright or inverted; (4) a steady-state visual evoked potential (SSVEP) paradigm in which participants passively viewed a flickering screen (15 Hz) for 2 min; and (5) a resting state paradigm in which participants sat quietly with their eyes open and then closed for 3 min each. Results The MMN components and P300 peaks were equivalent between the two systems (BF10 = 0.25 and BF10 = 0.26, respectively), with high intraclass correlations (ICCs) between the ERP waveforms (>0.81). Although the N170 peak values recorded by the two systems were different (BF10 = 35.88), ICCs demonstrated that the N170 ERP waveforms were strongly correlated over the right hemisphere (P8; 0.87–0.97), and moderately-to-strongly correlated over the left hemisphere (P7; 0.52–0.84). For the SSVEP, the signal-to-noise ratio (SNR) was larger for Neuroscan than Emotiv EPOC Flex (19.94 vs. 8.98, BF10 = 51,764), but SNR z-scores indicated a significant brain response at the stimulus frequency for both Neuroscan (z = 12.47) and Flex (z = 11.22). In the resting state task, both systems measured similar alpha power (BF10 = 0.28) and higher alpha power when the eyes were closed than open (BF10 = 32.27). Conclusions The saline version of the Emotiv EPOC Flex captures data similar to that of a research-grade EEG system. It can be used to measure reliable auditory and visual research-quality ERPs. In addition, it can index SSVEP signatures and is sensitive to changes in alpha oscillations

Self-concept in poor readers : A systematic review and meta-analysis

Background The aims of this systematic review and meta-analyses were to determine if there is a statistically reliable association between poor reading and poor self-concept, and if such an association is moderated by domain of self-concept, type of reading impairment, or contextual factors including age, gender, reading instruction, and school environment. Methodology We searched 10 key databases for published and unpublished studies, as well as reference lists of included studies, and studies that cited included studies. We calculated standardised mean differences (SMDs) and 95% confidence intervals for one primary outcome (average self-concept) and 10 secondary outcomes (10 domains of self-concept). We assessed the data for risk of bias, heterogeneity, sensitivity, reporting bias, and quality of evidence. Results Thirteen studies with 3,348 participants met our selection criteria. Meta-analyses revealed statistically significant SMDs for average self-concept (−0.57) and five domains of self-concept (reading/writing/spelling: −1.03; academic: −0.67; math: −0.64; behaviour: −0.32; physical appearance: −0.28). The quality of evidence for the primary outcome was moderate, and for secondary outcomes was low, due to lack of data. Conclusions These outcomes suggest a probable moderate association between poor reading and average self-concept; a possible strong association between poor reading and reading-writing-spelling self-concept; and possible moderate associations between poor reading and self-concept in the self-concept domains of academia, mathematics, behaviour, and physical appearance

Phonics training for English-speaking poor readers (Review)

Background The reading skills of 16% of children fall below the mean range for their age, and 5% of children have significant and severe reading problems. Phonics training is one of the most common reading treatments used with poor readers, particularly children. Objectives To measure the effect of phonics training and explore the impact of various factors, such as training duration and training group size, that might moderate the effect of phonics training on literacy‐related skills in English‐speaking poor readers. Search methods We searched CENTRAL, MEDLINE, Embase, 12 other databases, and three trials registers up to May 2018. We also searched reference lists of included studies and contacted experts in the field to identify additional studies. Selection criteria We included studies that used randomisation, quasi‐randomisation, or minimisation to allocate participants to a phonics intervention group (phonics training only or phonics training plus one other literacy‐related skill) or a control group (no training or non‐literacy training). Participants were English‐speaking poor readers with word reading one standard deviation below the appropriate level for their age (children, adolescents, and adults) or one grade or year below the appropriate level (children only), for no known reason. Participants had no known comorbid developmental disorder, or physical, neurological, or emotional problem. Data collection and analysis We used standard methodological procedures expected by Cochrane. Main results We included 14 studies with 923 participants in this review. Studies took place in Australia, Canada, the UK, and the USA. Six of the 14 included studies were funded by government agencies and one was funded by a university grant. The rest were funded by charitable foundations or trusts. Each study compared phonics training alone, or in conjunction with one other reading‐related skill, to either no training (i.e. treatment as usual) or alterative training (e.g. maths). Participants were English‐speaking children or adolescents, of low and middle socioeconomic status, whose reading was one year, one grade, or one standard deviation below the level expected for their age or grade for no known reason. Phonics training varied between studies in intensity (up to four hours per week), duration (up to seven months), training group size (individual and small groups), and delivery (human and computer). We measured the effect of phonics training on seven primary outcomes (mixed/regular word reading accuracy, non‐word reading accuracy, irregular word reading accuracy, mixed/regular word reading fluency, non‐word reading fluency, reading comprehension, and spelling). We judged all studies to be at low risk of bias for most risk criteria, and used the GRADE approach to assess the quality of the evidence. There was low‐quality evidence that phonics training may have improved poor readers' accuracy for reading real and novel words that follow the letter‐sound rules (standardised mean difference (SMD) 0.51, 95% confidence interval (CI) 0.13 to 0.90; 11 studies, 701 participants), and their accuracy for reading words that did not follow these rules (SMD 0.67, 95% CI 0.26 to 1.07; 10 studies, 682 participants). There was moderate‐quality evidence that phonics training probably improved English‐speaking poor readers' fluency for reading words that followed the letter‐sounds rules (SMD 0.45, 95% CI 0.19 to 0.72; 4 studies, 224 participants), and non‐word reading fluency (SMD 0.39, 95% CI 0.10 to 0.68; 3 studies, 188 participants), as well as their accuracy for reading words that did not follow these rules (SMD 0.84, 95% CI 0.30 to 1.39; 4 studies, 294 participants). In addition, there was low‐quality evidence that phonics training may have improved poor readers' spelling (SMD 0.47, 95% CI –0.07 to 1.01; 3 studies, 158 participants), but only slightly improve their reading comprehension (SMD 0.28, 95% CI –0.07 to 0.62; 5 studies, 343 participants). Authors' conclusions Phonics training appears to be effective for improving literacy‐related skills, particularly reading fluency of words and non‐words, and accuracy of reading irregular words. More studies are needed to improve the precision of outcomes, including word and non‐word reading accuracy, reading comprehension, spelling, letter‐sound knowledge, and phonological output. More data are also needed to determine if phonics training in English‐speaking poor readers is moderated by factors such as training type, intensity, duration, group size, or administrator

An interdisciplinary approach to enhance children’s listening, learning, and wellbeing in the classroom : The Listen to Learn for Life (L3) Assessment Framework

Introduction: Listening is the gateway to children learning in the mainstream classroom. However, modern classrooms are noisy and dynamic environments making listening challenging. It is therefore critical for researchers from speech and hearing, education, and health sciences to co-design and collaborate to realistically assess how children listen to learn in the classroom and to understand how listening can be improved to enhance children’s learning and wellbeing – an understanding which is currently lacking. Such highly interdisciplinary thinking demands a holistic classroom listening framework that can integrate a range of varied assessments and outcomes. Methods: An extensive review of literature into classroom listening was conducted but failed to identify a suitable framework. In this hypothesis and theory article we present a new framework that we have developed – the Listen to Learn for Life (L3) Assessment Framework. Results: The L3 Assessment Framework holistically incorporates frameworks from health, speech and hearing sciences, and education sectors. The framework accommodates a broad range of different factors that may affect listening, allowing for researchers to choose specific factors dependent on the context of use. Discussion: Selected examples of applying the framework are provided demonstrating how to assess children’s performance during different classroom activities as well as the effectiveness of a chosen intervention. For example, the framework can be used to assess the effectiveness of a wireless remote microphone intervention during group work activities for a child with autism. Conclusion: The L3 Assessment Framework provides a theoretical basis for the future development of research and practice as applied to listening in a classroom setting

Peripheral preview abolishes N170 face-sensitivity at fixation: Using fixation-related potentials to investigate dynamic face processing

The N170 ERP peak has been found to be consistently larger in response to the presentation of faces than to other objects, yet it is not clear whether this face-sensitive N170 is also elicited during fixations made subsequent to the initial presentation. To investigate this question, the current study utilised Event and Fixation-Related Potentials in two experiments, time-locking brain potentials to the presentation of faces and objects (watches) images in participants’ peripheral vision, and to their first fixations on the images. Experiment 1 found that a face-sensitive N170 was elicited by the onset of images but not by a subsequent fixation on the images, and that face inversion did not modulate N170 beyond presentation. Experiment 2 found that disrupting the structure of the peripheral preview (phase-scrambling) led to a face-sensitive N170 at fixation onsets on the intact-images. Interestingly, N170 amplitudes for both faces and objects were significantly enhanced after the peripheral preview was phase-scrambled, suggesting that the N170 in part reflects a category-detection process that is elicited once when an image structure is viewed. These results indicate that neural processing during fixations will be significantly modulated when they are immediately preceded by peripheral previews, and is not specific to faces

Frequency of educational computer use as a longitudinal predictor of educational outcomes in young people with specific language impairment

Computer use draws on linguistic abilities. Using this medium thus presents challenges for young people with Specific Language Impairment (SLI) and raises questions of whether computer-based tasks are appropriate for them. We consider theoretical arguments predicting impaired performance and negative outcomes relative to peers without SLI versus the possibility of positive gains. We examine the relationship between frequency of computer use (for leisure and educational purposes) and educational achievement; in particular examination performance at the end of compulsory education and level of educational progress two years later. Participants were 49 young people with SLI and 56 typically developing (TD) young people. At around age 17, the two groups did not differ in frequency of educational computer use or leisure computer use. There were no associations between computer use and educational outcomes in the TD group. In the SLI group, after PIQ was controlled for, educational computer use at around 17 years of age contributed substantially to the prediction of educational progress at 19 years. The findings suggest that educational uses of computers are conducive to educational progress in young people with SLI