The consequences of the new European reclassification of non-invasive brain stimulation devices and the medical device regulations pose an existential threat to research and treatment: An invited opinion paper.

A significant amount of European basic and clinical neuroscience research includes the use of transcranial magnetic stimulation (TMS) and low intensity transcranial electrical stimulation (tES), mainly transcranial direct current stimulation (tDCS). Two recent changes in the EU regulations, the introduction of the Medical Device Regulation (MDR) (2017/745) and the Annex XVI have caused significant problems and confusions in the brain stimulation field. The negative consequences of the MDR for non-invasive brain stimulation (NIBS) have been largely overlooked and until today, have not been consequently addressed by National Competent Authorities, local ethical committees, politicians and by the scientific communities. In addition, a rushed bureaucratic decision led to seemingly wrong classification of NIBS products without an intended medical purpose into the same risk group III as invasive stimulators. Overregulation is detrimental for any research and for future developments, therefore researchers, clinicians, industry, patient representatives and an ethicist were invited to contribute to this document with the aim of starting a constructive dialogue and enacting positive changes in the regulatory environment.

Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles.

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of clinical and research applications. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to time consuming and cost ineffective treatment development phases. We propose the combination of electroencephalography (EEG) and unsupervised learning for the stratification and prediction of individual responses to tDCS. A randomized, sham-controlled, double-blind crossover study design was conducted within a clinical trial for the development of pediatric treatments based on tDCS. The tDCS stimulation (sham and active) was applied either in the left dorsolateral prefrontal cortex or in the right inferior frontal gyrus. Following the stimulation session, participants performed 3 cognitive tasks to assess the response to the intervention: the Flanker Task, N-Back Task and Continuous Performance Test (CPT). We used data from 56 healthy children and adolescents to implement an unsupervised clustering approach that stratify participants based on their resting-state EEG spectral features before the tDCS intervention. We then applied a correlational analysis to characterize the clusters of EEG profiles in terms of participant's difference in the behavioral outcome (accuracy and response time) of the cognitive tasks when performed after a tDCS-sham or a tDCS-active session. Better behavioral performance following the active tDCS session compared to the sham tDCS session is considered a positive intervention response, whilst the reverse is considered a negative one. Optimal results in terms of validity measures was obtained for 4 clusters. These results show that specific EEG-based digital phenotypes can be associated to particular responses. While one cluster presents neurotypical EEG activity, the remaining clusters present non-typical EEG characteristics, which seem to be associated with a positive response. Findings suggest that unsupervised machine learning can be successfully used to stratify and eventually predict responses of individuals to a tDCS treatment.

BRAINCODE for Cognitive Impairment Diagnosis in Older Adults: Designing a Case-Control Pilot Study.

An early, extensive, accurate, and cost-effective clinical diagnosis of neurocognitive disorders will have advantages for older people and their families, but also for the health and care systems sustainability and performance. BRAINCODE is a technology that assesses cognitive impairment in older people, differentiating normal from pathologic brain condition, based in an EEG biomarkers evaluation. This paper will address BRAINCODE's pilot design, which intends to validate its efficacy, to provide guidelines for future studies and to allow its integration on the SHAPES platform. It is expected that BRAINCODE confirms a regular clinical diagnosis and neuropsychologic tests to discriminate 'normal' from pathologic cognitive decline and differentiates mild cognitive impairment from dementia in older adults with/without subjective cognitive complains.

A novel closed-loop EEG-tDCS approach to promote responsiveness of patients in minimally conscious state: A study protocol.

Transcranial direct current stimulation (tDCS) applied over the prefrontal cortex has been shown to improve behavioral responsiveness in patients with disorders of consciousness following severe brain injury, especially those in minimally conscious state (MCS). However, one potential barrier of clinical response to tDCS is the timing of stimulation with regard to the fluctuations of vigilance that characterize this population. Indeed, a previous study showed that the vigilance of MCS patients has periodic average cycles of 70 min (range 57-80 min), potentially preventing them to be in an optimal neural state to benefit from tDCS when applied randomly. To tackle this issue, we propose a new protocol to optimize the application of tDCS by selectively stimulating at high and low vigilance states. Electroencephalography (EEG) real-time spectral entropy will be used as a marker of vigilance and to trigger tDCS, in a closed-loop fashion. We will conduct a randomized controlled crossover clinical trial on 16 patients in prolonged MCS who will undergo three EEG-tDCS sessions 5 days apart (1. tDCS applied at high vigilance; 2. tDCS applied at low vigilance; 3. tDCS applied at a random moment). Behavioral effects will be assessed using the Coma Recovery Scale-Revised at baseline and right after the stimulations. EEG will be recorded throughout the session and for 30 min after the end of the stimulation. This unique and novel approach will provide patients' tailored treatment options, currently lacking in the field of disorders of consciousness.

Behavioral and electrophysiological effects of network-based frontoparietal tDCS in patients with severe brain injury: A randomized controlled trial.

BACKGROUND: Transcranial direct current stimulation (tDCS) may promote the recovery of severely brain-injured patients with disorders of consciousness (DOC). Prior tDCS studies targeted single brain regions rather than brain networks critical for consciousness recovery. OBJECTIVE: Investigate the behavioral and electrophysiological effects of multifocal tDCS applied over the frontoparietal external awareness network in patients with chronic acquired DOC. METHODS: Forty-six patients were included in this randomized double-blind sham-controlled crossover trial (median [interquartile range]: 46 [35 - 59] years old; 12 [5 - 47] months post injury; 17 unresponsive wakefulness syndrome, 23 minimally conscious state (MCS) and 6 emerged from the MCS). Multifocal tDCS was applied for 20 min using 4 anodes and 4 cathodes with 1 mA per electrode. Coma Recovery Scale-Revised (CRS-R) assessment and 10 min of resting state electroencephalogram (EEG) recordings were acquired before and after the active and sham sessions. RESULTS: At the group level, there was no tDCS behavioral treatment effect. However, following active tDCS, the EEG complexity significantly increased in low frequency bands (1-8 Hz). CRS-R total score improvement was associated with decreased baseline complexity in those bands. At the individual level, after active tDCS, new behaviors consistent with conscious awareness emerged in 5 patients. Conversely, 3 patients lost behaviors consistent with conscious awareness. CONCLUSION: The behavioral effect of multifocal frontoparietal tDCS varies across patients with DOC. Electrophysiological changes were observed in low frequency bands but not translated into behavioral changes at the group level.

Intermittent tACS during a visual task impacts neural oscillations and LZW complexity.

Little is known about how transcranial alternating current stimulation (tACS) interacts with brain activity. Here, we investigate the effects of tACS using an intermittent tACS-EEG protocol and use, in addition to classical metrics, Lempel-Ziv-Welch complexity (LZW) to characterize the interactions between task, endogenous and exogenous oscillations. In a cross-over study, EEG was recorded from thirty participants engaged in a change-of-speed detection task while receiving multichannel tACS over the visual cortex at 10 Hz, 70 Hz and a control condition. In each session, tACS was applied intermittently during 5 s events interleaved with EEG recordings over multiple trials. We found that, with respect to control, stimulation at 10 Hz ([Formula: see text]) enhanced both [Formula: see text] and [Formula: see text] power, [Formula: see text]-LZW complexity and [Formula: see text] but not [Formula: see text] phase locking value with respect to tACS onset ([Formula: see text]-PLV, [Formula: see text]-PLV), and increased reaction time (RT). [Formula: see text] increased RT with little impact on other metrics. As trials associated with larger [Formula: see text]-power (and lower [Formula: see text]-LZW) predicted shorter RT, we argue that [Formula: see text] produces a disruption of functionally relevant fast oscillations through an increase in [Formula: see text]-band power, slowing behavioural responses and increasing the complexity of gamma oscillations. Our study highlights the complex interaction between tACS and endogenous brain dynamics, and suggests the use of algorithmic complexity inspired metrics to characterize cortical dynamics in a behaviorally relevant timescale.

Prefrontal Cortex Neuromodulation Enhances Frontal Asymmetry and Reduces Caloric Intake in Patients with Morbid Obesity.

OBJECTIVE: The objective of this study was to test the feasibility of a combined intervention involving transcranial direct current stimulation (tDCS) on the dorsolateral prefrontal cortex (dlPFC) and cognitive training (CT). Short-term effects on food consumption, cognition, endocannabinoid (eCB) levels, and electroencephalogram (EEG) markers of future weight loss were explored. METHODS: Eighteen healthy volunteers with morbid obesity were randomized in a double-blind, placebo-controlled, parallel trial. Participants received sham or active tDCS plus CT for four consecutive days. Cognitive performance, daily food intake, and eCB blood samples were collected before and after the intervention; EEG data were gathered before and after daily training. RESULTS: The active tDCS + CT group reversed left-dominant frontal asymmetry and increased frontal coherence (FC) in the γ-band (30-45 Hz) after the intervention. The strength of the latter predicted BMI reduction. Additionally, a large intervention effect on food intake was shown in the active tDCS + CT group at follow-up (-339.6 ± 639 kcal on average), and there was a decrease of plasma eCB concentrations. CONCLUSIONS: dlPFC modulation through tDCS + CT is an effective tool to restore right dominance of the dlPFC and enhance FC in patients with morbid obesity. Moreover, the effect of the strength of FC on BMI suggests that the interhemispheric FC at the dlPFC is functionally relevant for the efficient regulation of food choice.

Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder.

REM Behavior Disorder (RBD) is now recognized as the prodromal stage of α-synucleinopathies such as Parkinson's disease (PD). In this paper, we describe deep learning models for diagnosis/prognosis derived from a few minutes of eyes-closed resting electroencephalography data (EEG) collected at baseline from idiopathic RBD patients (n = 121) and healthy controls (HC, n = 91). A few years after the EEG acquisition (4±2 years), a subset of the RBD patients were eventually diagnosed with either PD (n = 14) or Dementia with Lewy bodies (DLB, n = 13), while the rest remained idiopathic RBD. We describe first a simple deep convolutional neural network (DCNN) with a five-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG spectrograms from idiopathic patients and healthy controls. We treat the data as in audio or image classification problems where deep networks have proven successful by exploiting invariances and compositional features in the data. For comparison, we study a simple deep recurrent neural network (RNN) model using three stacked Long Short Term Memory network (LSTM) cells or gated-recurrent unit (GRU) cells-with very similar results. The performance of these networks typically reaches 80% (±1%) classification accuracy in the balanced HC vs. PD-conversion classification problem. In particular, using data from the best single EEG channel, we obtain an area under the curve (AUC) of 87% (±1%)-while avoiding spectral feature selection. The trained classifier can also be used to generate synthetic spectrograms using the DeepDream algorithm to study what time-frequency features are relevant for classification. We find these to be bursts in the theta band together with a decrease of bursting in the alpha band in future RBD converters (i.e., converting to PD or DLB in the follow up) relative to HCs. From this first study, we conclude that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, offering physiological insights and enabling the identification of clinically relevant biomarkers.

Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks.

State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that is produced by a repetitive visual stimulus. SSVEPs have long been considered a steady-state response resulting from purely oscillatory components phase locked with the stimulation source, matching the stimulation frequency and its harmonics. Here we explore the dynamical character of the SSVEP response by proposing a novel non-stationary methodology for SSVEP detection based on an ensemble of Echo State Networks (ESN). The performance of this dynamical approach is compared to stationary canonical correlation analysis (CCA) for the detection of 6 visual stimulation frequencies ranging from 12 to 22 Hz. ESN-based methodology outperformed CCA, achieving an average information transfer rate of 47 bits/minute when simulating a BCI system of 6 degrees of freedom. However, for some subjects and stimulation frequencies the detection accuracy of CCA exceeds that of ESN. The comparison suggests that each methodology captures different features of the SSVEP response: while CCA captures purely stationary patterns, the ESN-based approach presented here is capable of detecting the non-stationary nature of the SSVEP.

Algorithmic Complexity of EEG for Prognosis of Neurodegeneration in Idiopathic Rapid Eye Movement Behavior Disorder (RBD).

Idiopathic rapid eye movement sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinson's disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis. We analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from ~ 3 min recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression. Complexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels. Poor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude algorithmic coupling. Algorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.

Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.

The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

The INTERGROWTH-21st Project Neurodevelopment Package: a novel method for the multi-dimensional assessment of neurodevelopment in pre-school age children.

BACKGROUND: The International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) Project is a population-based, longitudinal study describing early growth and development in an optimally healthy cohort of 4607 mothers and newborns. At 24 months, children are assessed for neurodevelopmental outcomes with the INTERGROWTH-21st Neurodevelopment Package. This paper describes neurodevelopment tools for preschoolers and the systematic approach leading to the development of the Package. METHODS: An advisory panel shortlisted project-specific criteria (such as multi-dimensional assessments and suitability for international populations) to be fulfilled by a neurodevelopment instrument. A literature review of well-established tools for preschoolers revealed 47 candidates, none of which fulfilled all the project's criteria. A multi-dimensional assessment was, therefore, compiled using a package-based approach by: (i) categorizing desired outcomes into domains, (ii) devising domain-specific criteria for tool selection, and (iii) selecting the most appropriate measure for each domain. RESULTS: The Package measures vision (Cardiff tests); cortical auditory processing (auditory evoked potentials to a novelty oddball paradigm); and cognition, language skills, behavior, motor skills and attention (the INTERGROWTH-21st Neurodevelopment Assessment) in 35-45 minutes. Sleep-wake patterns (actigraphy) are also assessed. Tablet-based applications with integrated quality checks and automated, wireless electroencephalography make the Package easy to administer in the field by non-specialist staff. The Package is in use in Brazil, India, Italy, Kenya and the United Kingdom. CONCLUSIONS: The INTERGROWTH-21st Neurodevelopment Package is a multi-dimensional instrument measuring early child development (ECD). Its developmental approach may be useful to those involved in large-scale ECD research and surveillance efforts.

AsTeRICS: a new flexible solution for people with motor disabilities in upper limbs and its implication for rehabilitation procedures.

PURPOSE: To present the AsTeRICS construction set, and examine different combinations of sensors installed in the platform and how users interact with them. METHOD: Nearly 50 participants from Austria, Poland and Spain were included in the study. They had a heterogeneous range of diagnoses, but as a common feature all of them experienced motor limitations in their upper limbs. The study included a 1 h session with each participant where the user interacted with a personalized combination of sensors, based on a previous assessment on their motor capabilities performed by healthcare professionals. The sensors worked as substitutes for a standard QWERTY keyboard and a standard mouse. Semi-structured interviews were conducted to obtain participants' opinions. All collected data were analyzed based on the qualitative methodology. RESULTS: The findings illustrated that AsTeRICS is a flexible platform whose sensors can adapt to different degrees of users' motor capabilities, thus facilitating in most cases the interaction of the participants with a common computer. CONCLUSION: AsTeRICS platform can improve the interaction between people with mobility limitations and computers. It can provide access to new technologies and become a promising tool that can be integrated in physical rehabilitation programs for people with motor disabilities in their upper limbs. IMPLICATIONS FOR REHABILITATION: The AsTeRICS platform offers an interesting tool to interface and support the computerized rehabilitation program of the patients. Due to AsTeRICS platform high usability features, family and rehabilitation professionals can learn how to use the AsTeRICS platform quickly fostering the key role of their involvement on patients' rehabilitation. AsTeRICS is a flexible, extendable, adaptable and affordable technology adapted for using computer, environmental control, mobile phone, rehabilitation programs and mechatronic systems. AsTeRICS makes possible an easy reconfiguration and integration of new functionalities, such as biofeedback rehabilitation, without major changes in the system.

Transcranial current brain stimulation (tCS): models and technologies.

In this paper, we provide a broad overview of models and technologies pertaining to transcranial current brain stimulation (tCS), a family of related noninvasive techniques including direct current (tDCS), alternating current (tACS), and random noise current stimulation (tRNS). These techniques are based on the delivery of weak currents through the scalp (with electrode current intensity to area ratios of about 0.3-5 A/m2) at low frequencies (typically < 1 kHz) resulting in weak electric fields in the brain (with amplitudes of about 0.2-2 V/m). Here we review the biophysics and simulation of noninvasive, current-controlled generation of electric fields in the human brain and the models for the interaction of these electric fields with neurons, including a survey of in vitro and in vivo related studies. Finally, we outline directions for future fundamental and technological research.

Electro-physiological data fusion for stress detection.

In this work we describe the performance evaluation of a system for stress detection. The analysed data is acquired by following an experimental protocol designed to induce cognitive stress to the subjects. The experimental set-up included the recording of electroencephalography (EEG) and facial (corrugator and zygomatic) electromyography (EMG). In a preliminary analysis we are able to correlate EEG features (alpha asymmetry and alpha/beta ratio using only 3 channels) with the stress level of the subjects statistically (by using averages over subjects) but also on a subject-to-subject basis by using computational intelligence techniques reaching classification rates up to 79% when classifying 3 minutes takes. On a second step, we apply fusion techniques to the overall multi-modal feature set fusing the formerly mentioned EEG features with EMG energy. We show that the results improve significantly providing a more robust stress index every second. Given the achieved performance the system described in this work can be successfully applied for stress therapy when combined with virtual reality.

A rapid technique for classifying phytoplankton fluorescence spectra based on self-organizing maps.

Fluorescence spectroscopy has been demonstrated to be a powerful tool for characterizing phytoplankton communities in marine environments. Using different fluorescence spectra techniques, it is now possible to discriminate the major phytoplankton groups. However, most of the current techniques are based on fluorescence excitation measurements, which require stimulation at different wavelengths and thus considerable time to obtain the complete spectral profile. This requirement may be an important constraint for several mobile oceanographic platforms, such as vertical profilers or autonomous underwater vehicles, which require rapid-acquisition instruments. This paper presents a novel technique for classifying fluorescence spectra based on self-organizing maps (SOMs), one of the most popular artificial neural network (ANN) methods. The method is able to achieve phytoplankton discrimination using only fluorescence emission spectra (single wavelength excitation), thus reducing the acquisition time. The discrimination capabilities of SOM using excitation and emission spectra are compared. The analysis shows that the SOM has a good performance using excitation spectra, whereas data preprocessing is required in order to obtain similar discrimination capabilities using emission spectra. The final results obtained using emission spectra indicate that the discrimination is properly achieved even between algal groups, such as diatoms and dinoflagellates, which cannot be discriminated with previous methods. We finally point out that although techniques based on excitation spectra can achieve a better taxonomic accuracy, there are some applications that require faster acquisition processes. Acquiring emission spectra is almost instantaneous, and techniques such as SOM can achieve good classification performance using appropriately preprocessed data.