Dry EEG Electrodes Research Articles

A 382nVrms 100GΩ@50Hz Active Electrode for Dry-Electrode EEG Recording.

This article describes a low noise and ultra-high input impedance active electrode (AE) interface chip for dry-electrode EEG recording. To compensate the input parasitic capacitance and the ESD leakage, power/ground/ESD bootstrapping is proposed. This design integrates chopping stabilization technique to suppress flicker noise of the amplifier which has never been tackled in previous bootstrapped AE design. Both on-chip and off-chip input routing is active shielded to minimize wire parasitic. Fabricated in a 0.18μm CMOS process, the AE core occupies about 0.056mm2 and draws 17.95μA from a 1.8V supply. The proposed AE achieves 100GΩ input impedance at 50Hz and over 1GΩ at 1kHz with a low input-referred noise of 382nVrms integrated from 0.5Hz to 70Hz. This design is the first 100GΩ@50Hz input impedance chopper stabilized AE compared to the state-of-the-art. Dry-electrode EEG recording capability of the proposed AE are verified on three types of experiments including spontaneous α-wave, event related potential and steady-state visual evoked potential.

WalkingWizard—A Truly Wearable EEG Headset for Everyday Use

Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in the face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG and commercially available dry EEG headsets. We then tested WalkingWizard using steady-state visual evoked potential (SSVEP) experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0 km/h, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly—realizing WalkingWizard Integrated , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: classification of SSVEP in VR environment while walking, real-time acquisition of emotional state of users while moving around the neighbourhood, and understanding the effect of guided meditation for relaxation.

An 800MΩ-Input-Impedance 95.3dB-DR Δ-ΔΣ AFE for Dry-Electrode Wearable EEG Recording.

Non-invasive, closed-loop brain modulation offers an accessible and cost-effective means of evaluating and modulating one's mental and physical well-being, such as Parkinson's disease, epilepsy, and sleep disorders. However, wearable EEG systems pose significant challenges for the analog front-end (AFE) circuits in view of µV-level EEG signals of interest, multiple sources of interference, and ill-defined skin contact. This paper presents a direct-digitization AFE tailored for dry-electrode scalp EEG recording, characterized by wide input dynamic range (DR) and high input impedance. The AFE utilizes a second-order 5-bit delta-delta sigma (Δ-ΔΣ) ADC to shape DC electrode offset (DEO) and low-frequency disturbances while retaining high accuracy. A non-inverting pseudo-differential instrumentation amplifier (IA) embedded in the ADC ensures high input impedance (Zin) and common-mode rejection ratio (CMRR). Fabricated in a standard 0.18-μm CMOS process, the AFE delivers 700-mVpp input signal range, 95.3-dB DR, 87-dB SNDR, and 800-MΩ input impedance at 50 Hz while consuming 88.4µW from a 1.2 V supply. The benefits of high DR and high input impedance have been validated by dry-electrode EEG measurement.

Simultaneous Dry and Gel-Based High-Density Electroencephalography Recordings.

Evaluations of new dry, high-density EEG caps have only been performed so far with serial measurements and not with simultaneous (parallel) measurements. For a first comparison of gel-based and dry electrode performance in simultaneous high-density EEG measurements, we developed a new EEG cap comprising 64 gel-based and 64 dry electrodes and performed simultaneous measurements on ten volunteers. We analyzed electrode-skin impedances, resting state EEG, triggered eye blinks, and visual evoked potentials (VEPs). To overcome the issue of different electrode positions in the comparison of simultaneous measurements, we performed spatial frequency analysis of the simultaneously measured EEGs using spatial harmonic analysis (SPHARA). The impedances were 516 ± 429 kOhm (mean ± std) for the dry electrodes and 14 ± 8 kOhm for the gel-based electrodes. For the dry EEG electrodes, we obtained a channel reliability of 77%. We observed no differences between dry and gel-based recordings for the alpha peak frequency and the alpha power amplitude, as well as for the VEP peak amplitudes and latencies. For the VEP, the RMSD and the correlation coefficient between the gel-based and dry recordings were 1.7 ± 0.7 μV and 0.97 ± 0.03, respectively. We observed no differences in the cumulative power distributions of the spatial frequency components for the N75 and P100 VEP peaks. The differences for the N145 VEP peak were attributed to the different noise characteristics of gel-based and dry recordings. In conclusion, we provide evidence for the equivalence of simultaneous dry and gel-based high-density EEG measurements.

Posttraumatic growth EEG neuromarkers: translational neural comparisons with resilience and PTSD in trauma-exposed healthy adults

ABSTRACT Background: Supporting wellbeing beyond symptom reduction is necessary in trauma care. Research suggests increased posttraumatic growth (PTG) may promote wellbeing more effectively than posttraumatic stress disorder (PTSD) symptom reduction alone. Understanding neurobiological mechanisms of PTG would support PTG intervention development. However, most PTG research to-date has been cross-sectional data self-reported through surveys or interviews. Objective: Neural evidence of PTG and its coexistence with resilience and PTSD is limited. To advance neural PTG literature and contribute translational neuroscientific knowledge necessary to develop future objectively measurable neural-based PTG interventions. Method: Alpha frequency EEG and validated psychological inventories measuring PTG, resilience, and PTSD symptoms were collected from 30 trauma-exposed healthy adults amidst the COVID-19 pandemic. EEG data were collected using custom MNE-Python software, and a wireless OpenBCI 16-channel dry electrode EEG headset. Psychological inventory scores were analysed in SPSS Statistics and used to categorise the EEG data. Power spectral density analyses, t-tests and ANOVAs were conducted within EEGLab to identify brain activity differentiating high and low PTG, resilience, and PTSD symptoms. Results: Higher PTG was significantly differentiated from low PTG by higher alpha power in the left centro-temporal brain area around EEG electrode C3. A trend differentiating high PTG from PTSD was also indicated in this same location. Whole-scalp spectral topographies revealed alpha power EEG correlates of PTG, resilience and PTSD symptoms shared limited, but potentially meaningful similarities. Conclusion: This research provides the first comparative neural topographies of PTG, resilience and PTSD symptoms in the known literature. Results provide objective neural evidence supporting existing theory depicting PTG, resilience and PTSD as independent, yet co-occurring constructs. PTG neuromarker alpha C3 significantly delineated high from low PTG and warrants further investigation for potential clinical application. Findings provide foundation for future neural-based interventions and research for enhancing PTG in trauma-exposed individuals.

Loop Fabric EEG Textrode for Brain Activity Monitoring

Recently, metallic dry EEG electrodes have been introduced to overcome the limitation of wet electrodes, as the conductive gel used causes skin irritation and dries out over time. However, the metal dry EEG electrodes have a high weight and a stiff structure that limits them from wearable application. In this work, we have developed a textile-based EEG electrode (textrode) from silver-plated polyamide loop fabric washable up to 100 cycles. The new EEG textrode collects quality signals comparable to commercial dry Ag/AgCl EEG electrodes. The signals were detected at all major EEG bandwidths. In addition, the new textrodes showed a lower skin-to-electrode impedance (-19.23%) than the commercial dry electrode and a higher signal-to-noise ratio (+27.4%). Therefore, these novel textile-based electrodes can be used to monitor brain activity for wearable applications, especially when long-term monitoring is required.

Novel dry EEG electrode with composite filler of PEDOT:PSS and carbon particles.

We have developed a novel composite filler with Poly(3,4-ethylenedioxythiophene) : poly(styrenesulfonic acid) (PEDOT:PSS), a biocompatible organic conductive polymer, adsorbed on carbon particles for biological electrodes. This composite filler enables to fabricate high-performance biological electrodes simply by adding it to resin in the same way as conventional conductive fillers. The fabricated electrodes achieve ion exchange properties similar to those of PEDOT:PSS polymers and therefore low skin and electrode contact impedance. Electroencephalogram (EEG) measurements show that these electrodes capture various brain activities and exhibit high correlation (≥ 0.9) to commercially available wet and AgCl electrodes. Additionally, each electrode can be molded into various shapes and structures while retaining its electrode characteristics. Therefore, the proposed electrode is promising for EEG measurement, which requires high comfort and signal quality.

Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition.

Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (<150 kΩ) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver-silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10-20 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 kΩ. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol.

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring.

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

High performance clean versus artifact dry electrode EEG data classification using Convolutional Neural Network transfer learning

ObjectiveConvolutional Neural Networks (CNNs) are promising for artifact detection in electroencephalography (EEG) data, but require large amounts of data. Despite increasing use of dry electrodes for EEG data acquisition, dry electrode EEG datasets are sparse. We aim to develop an algorithm for clean versus artifact dry electrode EEG data classification using transfer learning. MethodsDry electrode EEG data were acquired in 13 subjects while physiological and technical artifacts were induced. Data were per 2-second segment labeled as clean or artifact and split in an 80% train and 20% test set. With the train set, we fine-tuned a pre-trained CNN for clean versus artifact wet electrode EEG data classification using 3-fold cross validation. The three fine-tuned CNNs were combined in one final clean versus artifact classification algorithm, in which the majority vote was used for classification. We calculated accuracy, F1-score, precision, and recall of the pre-trained CNN and fine-tuned algorithm when applied to unseen test data. ResultsThe algorithm was trained on 0.40 million and tested on 0.17 million overlapping EEG segments. The pre-trained CNN had a test accuracy of 65.6%. The fine-tuned clean versus artifact classification algorithm had an improved test accuracy of 90.7%, F1-score of 90.2%, precision of 89.1% and recall of 91.2%. ConclusionsDespite a relatively small dry electrode EEG dataset, transfer learning enabled development of a high performing CNN-based algorithm for clean versus artifact classification. SignificanceDevelopment of CNNs for classification of dry electrode EEG data is challenging as dry electrode EEG datasets are sparse. Here, we show that transfer learning can be used to overcome this problem.

Correlating electroconvulsive therapy response to electroencephalographic markers: Study protocol.

Electroconvulsive therapy (ECT) is an effective intervention for patients with major depressive disorder (MDD). Despite longstanding use, the underlying mechanisms of ECT are unknown, and there are no objective prognostic biomarkers that are routinely used for ECT response. Two electroencephalographic (EEG) markers, sleep slow waves and sleep spindles, could address these needs. Both sleep microstructure EEG markers are associated with synaptic plasticity, implicated in memory consolidation, and have reduced expression in depressed individuals. We hypothesize that ECT alleviates depression through enhanced expression of sleep slow waves and sleep spindles, thereby facilitating synaptic reconfiguration in pathologic neural circuits. Correlating ECT Response to EEG Markers (CET-REM) is a single-center, prospective, observational investigation. Wireless wearable headbands with dry EEG electrodes will be utilized for at-home unattended sleep studies to allow calculation of quantitative measures of sleep slow waves (EEG SWA, 0.5-4 Hz power) and sleep spindles (density in number/minute). High-density EEG data will be acquired during ECT to quantify seizure markers. This innovative study focuses on the longitudinal relationships of sleep microstructure and ECT seizure markers over the treatment course. We anticipate that the results from this study will improve our understanding of ECT.

Neurological Outpatients Prefer EEG Home-Monitoring over Inpatient Monitoring-An Analysis Based on the UTAUT Model.

Home monitoring examinations offer diagnostic and economic advantages compared to inpatient monitoring. In addition, these technical solutions support the preservation of health care in rural areas in the absence of local care providers. The acceptance of patients is crucial for the implementation of home monitoring concepts. The present research assesses the preference for a health service that is to be introduced, namely an EEG home-monitoring of neurological outpatients—using a mobile, dry-electrode EEG (electroencephalography) system—in comparison to the traditional long-time EEG examination in a hospital. Results of a representative study for Germany (n = 421) reveal a preference for home monitoring. Importantly, this preference is partially driven by a video explaining the home monitoring system. We subsequently analyzed factors that influence the behavioral intention (BI) to use the new EEG system, drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The strongest positive predictor of BI is the belief that EEG home-monitoring will improve health quality, while computer anxiety and effort expectancy represent the strongest barriers. Furthermore, we find the UTAUT model’s behavioral intention construct to predict the patients’ decision for or against home monitoring more strongly than any other patient’s characteristic such as gender, health condition, or age, underlying the model’s usefulness.

ELECTRA-STROKE: Electroencephalography controlled triage in the ambulance for acute ischemic stroke-Study protocol for a diagnostic trial.

BackgroundEndovascular thrombectomy (EVT) is the standard treatment for large vessel occlusion stroke of the anterior circulation (LVO-a stroke). Approximately half of EVT-eligible patients are initially presented to hospitals that do not offer EVT. Subsequent inter-hospital transfer delays treatment, which negatively affects patients' prognosis. Prehospital identification of patients with LVO-a stroke would allow direct transportation of these patients to an EVT-capable center. Electroencephalography (EEG) may be suitable for this purpose because of its sensitivity to cerebral ischemia. The hypothesis of ELECTRA-STROKE is that dry electrode EEG is feasible for prehospital detection of LVO-a stroke.MethodsELECTRA-STROKE is an investigator-initiated, diagnostic study. EEG recordings will be performed in patients with a suspected stroke in the ambulance. The primary endpoint is the diagnostic accuracy of the theta/alpha ratio for the diagnosis of LVO-a stroke, expressed by the area under the receiver operating characteristic (ROC) curve. EEG recordings will be performed in 386 patients.DiscussionIf EEG can be used to identify LVO-a stroke patients with sufficiently high diagnostic accuracy, it may enable direct routing of these patients to an EVT-capable center, thereby reducing time-to-treatment and improving patient outcomes.Clinical trial registrationClinicalTrials.gov, identifier: NCT03699397.

Novel replaceable EEG electrode system

Abstract Due to the direct contact between electrode and scalp, dry EEG electrodes are exposed to increased mechanical wear compared to conventional gel-based electrodes. However, state-of-the-art commercial cap systems commonly use permanently fixated electrodes which can lead to downtime of the EEG cap during professional repair and replacement as well as reduced overall lifetime. An easily replaceable EEG electrode would furthermore improve hygiene, especially for newborn and infant applications. We propose a novel replaceable electrode system, consisting of an electrode holder, a snap top, a contact ring fixated inside the electrode holder, and a replaceable electrode. The production process consists of 3D printing, silicone molding, resin casting, and electroless plating. The replaceable electrode system is integrated into a multichannel EEG cap system. A verification study is conducted with 30 volunteers. The operators experienced that the new electrode holder eases adjustment of the electrode to have proper contact with the scalp. During the study, defective electrodes can be replaced without a soldering process. Furthermore, all electrodes stayed in the holder and did not fall off the cap for the whole session. In conclusion, the novel replaceable electrode system is suitable for EEG measurements.

A novel application of dry-electrode EEG Device for knowledge discovery in emotional-behavioral disorder studies

As per World Health Organization (WHO), emotional and behavioral disorders affect 10.7% of global population. Studies have shown emotional-behavioral problems having significant positive correlation with obesity and overweight (P&lt;0.01). It has been established in published body of knowledge that obesity and overweight are negatively correlated with psychological comorbidities, emotional and behavioral disorders, and self-esteem. There is an increasing interest in studying the psychological and cognitive neuroscience aspects of obesity. Lately there has been an increase in the number and availability of consumer grade low-cost EEG (electroencephalographic) devices. The objective of this study is two-fold i.e. exploring the research potential of a consumer grade EEG device (Muse) and analyzing the relationship between EEG electrical activity, depression and obesity. The level of depression was quantified through filling the PHQ-9 (patient health questionnaire) and students BMI (body mass index) noted. The EEG experiment consisted of displaying high and low-calorie visual stimuli along with non-food stimuli; with eyes closed in-between. This process was repeated, first in the hunger state and subsequently after taking a high sugar drink. During both experimental states EEG of the 10 student subjects was continuously recorded. On clustering the EEG recordings, sub clusters were discovered among minimally depressed students, but strong clustering was discovered among mildly and moderately depressed obese/overweight students with lower grades. Thus, mood and food-related information was found to be processed differently among subjects requiring monitoring vs. clinical judgment and consumer grade EEG device was found to have the potential to be used for practical research work.

Research on the Method of Depression Detection by Single-Channel Electroencephalography Sensor.

Depression is a common mental health illness worldwide that affects our quality of life and ability to work. Although prior research has used EEG signals to increase the accuracy to identify depression, the rates of underdiagnosis remain high, and novel methods are required to identify depression. In this study, we built a model based on single-channel, dry-electrode EEG sensor technology to detect state depression, which measures the intensity of depressive feelings and cognitions at a particular time. To test the accuracy of our model, we compared the results of our model with other commonly used methods for depression diagnosis, including the PHQ-9, Hamilton Depression Rating Scale (HAM-D), and House-Tree-Person (HTP) drawing test, in three different studies. In study 1, we compared the results of our model with PHQ-9 in a sample of 158 senior high students. The results showed that the consistency rate of the two methods was 61.4%. In study 2, the results of our model were compared with HAM-D among 71 adults. We found that the consistency rate of state-depression identification by the two methods was 63.38% when a HAM-D score above 7 was considered depression, while the consistency rate increased to 83.10% when subjects showed at least one depressive symptom (including depressed mood, guilt, suicide, lack of interest, retardation). In study 3, 68 adults participated in the study, and the results revealed that the consistency rate of our model and HTP drawing test was 91.2%. The results showed that our model is an effective means to identify state depression. Our study demonstrates that using our model, people with state depression could be identified in a timely manner and receive interventions or treatments, which may be helpful for the early detection of depression.

Feasibility assessment of patient-controlled EEG home-monitoring: More results from the HOMEONE study

ObjectiveThe feasibility phase of the HOME (Home-Monitoing and Education) project aims to show the practical feasibility of Electroencephalography (EEG)home-monitoring using a patient-controlled mobile system. Its objective is to assess the potential diagnostic and therapeutic yields of home-monitoring compared to conventional healthcare. Methods16 office-based practitioners chose 97 patients and recorded standard 20-minute EEGs using conventional recorders. After training, the same patients used a patient-controlled mobile dry electrode EEG system in their home environment. The practitioners in charge and two additional raters assessed all recordings. We conducted inter-rater and intra-rater comparisons between the diagnostic findings. Results89 patients successfully conducted home-monitoring recordings. The intra-rater comparison results for the diagnostic findings of the conventional recordings and the patient-made recordings show a fair Cohen’s kappa value (0.21). Additionally, we documented a change of patient management in 9 cases. ConclusionsThe feasibility of EEG home-monitoring using a patient-controlled device is confirmed. The yield of EEG home-monitoring comprises information that can influence patient management. SignificancePatient-controlled EEG home-monitoring is feasible as part of routine care for neurological outpatients as its technical efficacy and practical feasibility are shown and significantly positive effects on patient management are evidenced.

Online interaction method of mobile robot based on single-channel EEG signal and end-to-end CNN with residual block model

The development of alternative pathways to communicate with outside world independent on language or limb motions is important. However, one of the challenges is the multi-dimensional and accurate control of robot using head signals. This paper proposes an end-to-end CNN with residual block model, which uses the filtered raw signal as input and detects left eye blink, right eye blink, continuous eye blink, and grit teeth tasks, and develops an online brain–computer interface (BCI) system to control the left turn, right turn, forward, stop, and speed of the mobile robot platform, TurtleBot. The portable EEG measurement device TGAM module is used to collect single channel dry electrode EEG signals. Through time domain (TD) and time–frequency analysis, the time period and frequency range of each task signal are analyzed, which lays the foundation for analysis window parameter determination and feature extraction. The proposed model uses one-dimensional convolution to extract local features and two-dimensional convolution to extract global features, and the average detection accuracy is 97.399%, which is significantly higher than that of the state-of-the-art machine learning classifiers with the TD and frequency domain (FD) fusion features as input (p<0.01). The detection performance of FD features outperforms the TD features (p<0.01). Online BCI system based on the proposed method is developed to interact with the TurtleBot.

Older Adult Mild Cognitive Impairment Prediction from Multiscale Entropy EEG Patterns in Reminiscent Interior Image Working Memory Paradigm.

We discuss the practical employment of a machine learning (ML) technique within AI for a social good application. We present an application for elderly adult dementia onset prognostication. First, the paper explains our encouraging preliminary study results of EEG responses analysis using a signal complexity measure of multiscale entropy (MSE) in reminiscent interior working memory evaluation tasks. Then, we compare shallow and deep learning machine learning models for a digital biomarker of dementia onset detection. The evaluated machine-learning models succeed in the most reliable median accuracies above 80% using random forest and fully connected neural network classifiers in automatic discrimination of normal cognition versus a mild cognitive impairment (MCI) task. The classifier input features consist of MSE patterns only derived from four dry EEG electrodes. Fifteen elderly subjects voluntarily participate in the reported study focusing on EEG-based objective dementia biomarker advancement. The results showcase the essential social advantages of artificial intelligence (AI) application for the dementia prognosis and advance ML for the subsequent use for simple objective EEG-based examination.Clinical relevance- This manuscript introduces an objective biomarker from EEG recorded by a wearable for a plausible replacement of a mild cognitive impairment (MCI) evaluation using usual biased paper and pencil examinations.

Chronic Study on Brainwave Authentication in a Real-Life Setting: An LSTM-Based Bagging Approach.

With the advent of the digital age, concern about how to secure authorized access to sensitive data is increasing. Besides traditional authentication methods, there is an interest in biometric traits such as fingerprints, the iris, facial characteristics, and, recently, brainwaves, primarily based on electroencephalography (EEG). Current work on EEG-based authentication focuses on acute recordings in laboratory settings using high-end equipment, typically equipped with 64 channels and operating at a high sampling rate. In this work, we validated the feasibility of EEG-based authentication in a real-world, out-of-laboratory setting using a commercial dry-electrode EEG headset and chronic recordings on a population of 15 healthy people. We used an LSTM-based network with bootstrap aggregating (bagging) to decode our recordings in response to a multitask scheme consisting of performed and imagined motor tasks, and showed that it improved the performance of the standard LSTM approach. We achieved an authentication accuracy, false acceptance rate (FAR), and false rejection rate (FRR) of 92.6%, 2.5%, and 5.0% for the performed motor task; 92.5%, 2.6%, and 4.9% for the imagined motor task; and 93.0%, 1.9%, and 5.1% for the combined tasks, respectively. We recommend the proposed method for time- and data-limited scenarios.