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

A scoping review on the use of consumer-grade EEG devices for research

Background: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience. Purpose: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG. Methods: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author’s country. Results: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes. Conclusions: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices

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

The Antitumorigenic Function of EGFR in Metastatic Breast Cancer is Regulated by Expression of Mig6

Numerous studies by our lab and others demonstrate that epidermal growth factor receptor (EGFR) plays critical roles in primary breast cancer (BC) initiation, growth and dissemination. However, clinical trials targeting EGFR function in BC have lead to disappointing results. In the current study we sought to identify the mechanisms responsible for this disparity by investigating the function of EGFR across the continuum of the metastatic cascade. We previously established that overexpression of EGFR is sufficient for formation of in situ primary tumors by otherwise nontransformed murine mammary gland cells. Induction of epithelial-mesenchymal transition (EMT) is sufficient to drive the metastasis of these EGFR-transformed tumors. Examining growth factor receptor expression across this and other models revealed a potent downregulation of EGFR through metastatic progression. Consistent with diminution of EGFR following EMT and metastasis EGF stimulation changes from a proliferative to an apoptotic response in in situ versus metastatic tumor cells, respectively. Furthermore, overexpression of EGFR in metastatic MDA-MB-231 BC cells promoted their antitumorigenic response to EGF in three dimensional (3D) metastatic outgrowth assays. In line with the paradoxical function of EGFR through EMT and metastasis we demonstrate that the EGFR inhibitory molecule, Mitogen Induced Gene-6 (Mig6), is tumor suppressive in in situ tumor cells. However, Mig6 expression is absolutely required for prevention of apoptosis and ultimate metastasis of MDA-MB-231 cells. Further understanding of the paradoxical function of EGFR between primary and metastatic tumors will be essential for application of its targeted molecular therapies in BC

Should I Trust You? Autistic Traits Predict Reduced Appearance-Based Trust Decisions

Facial impressions of trustworthiness guide social decisions in the general population, as shown by financial lending in economic Trust Games. As an exception, autistic boys fail to use facial impressions to guide trust decisions, despite forming typical facial trustworthiness impressions (Ewing et al., 2015). Here, we tested whether this dissociation between forming and using facial impressions of trustworthiness extends to neurotypical men with high levels of autistic traits. Forty-six Caucasian men completed a multi-turn Trust Game, a facial trustworthiness impressions task, the Autism-Spectrum Quotient, and two Theory of Mind tasks. As hypothesized, participants’ levels of autistic traits had no observed effect on the impressions formed, but negatively predicted the use of those impressions in trust decisions. Thus, the dissociation between forming and using facial impressions of trustworthiness extends to the broader autism phenotype. More broadly, our results identify autistic traits as an important source of individual variation in the use of facial impressions to guide behaviour. Interestingly, failure to use these impressions could potentially represent rational behaviour, given their limited validity

A Bow Shock Nebula Around a Compact X-Ray Source in the Supernova Remnant IC443

We present spectra and high resolution images of the hard X-ray feature along the southern edge of the supernova remnant IC443. Data from the Chandra X-ray Observatory reveal a comet-shaped nebula of hard emission, which contains a softer point source at its apex. We also present 20cm, 6cm, and 3.5cm images from the Very Large Array that clearly show the cometary nebula. Based on the radio and X-ray morphology and spectrum, and the radio polarization properties, we argue that this object is a synchrotron nebula powered by the compact source that is physically associated with IC443. The spectrum of the soft point source is adequately but not uniquely fit by a black body model (kT=0.71 +/- 0.08 keV, L=(6.5 +/- 0.9) * 10^31 erg/s). The cometary morphology of the nebula is the result of the supersonic motion of the neutron star (V_NS=250 +/- 50 km/s), which causes the relativistic wind of the pulsar to terminate in a bow shock and trail behind as a synchrotron tail. This velocity is consistent with an age of 30,000 years for the SNR and its associated neutron star.Comment: 9 pages, 5 figures, accepted for publication in the ApJ Letter

Exploring Multimodal Large Language Models for Radiology Report Error-checking

This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-rays and CT scans). A subset of original reports was modified to contain synthetic errors by introducing three types of mistakes: "insert", "remove", and "substitute". The evaluation contained two difficulty levels: SIMPLE for binary error-checking and COMPLEX for identifying error types. At the SIMPLE level, our fine-tuned model significantly enhanced performance by 47.4% and 25.4% on MIMIC-CXR and IU X-ray data, respectively. This performance boost is also observed in unseen modality, CT scans, as the model performed 19.46% better than the baseline model. The model also surpassed the domain expert's accuracy in the MIMIC-CXR dataset by 1.67%. Notably, among the subsets (N=21) of the test set where a clinician did not achieve the correct conclusion, the LLaVA ensemble mode correctly identified 71.4% of these cases. However, all models performed poorly in identifying mistake types, underscoring the difficulty of the COMPLEX level. This study marks a promising step toward utilizing multimodal LLMs to enhance diagnostic accuracy in radiology. The ensemble model demonstrated comparable performance to clinicians, even capturing errors overlooked by humans