One-Year Follow-Up of Healthy Older Adults with Electroencephalographic Risk for Neurocognitive Disorder After Neurofeedback Training.

BACKGROUND: In healthy older adults, excess theta activity is an electroencephalographic (EEG) predictor of cognitive impairment. In a previous study, neurofeedback (NFB) treatment reinforcing reductions theta activity resulted in EEG reorganization and cognitive improvement. OBJECTIVE: To explore the clinical applicability of this NFB treatment, the present study performed a 1-year follow-up to determine its lasting effects. METHODS: Twenty seniors with excessive theta activity in their EEG were randomly assigned to the experimental or control group. The experimental group received an auditory reward when the theta absolute power (AP) was reduced. The control group received the reward randomly. RESULTS: Both groups showed a significant decrease in theta activity at the training electrode. However, the EEG results showed that only the experimental group underwent global changes after treatment. These changes consisted of delta and theta decreases and beta increases. Although no changes were found in any group during the period between the posttreatment evaluation and follow-up, more pronounced theta decreases and beta increases were observed in the experimental group when the follow-up and pretreatment measures were compared. Executive functions showed a tendency to improve two months after treatment which became significant one year later. CONCLUSION: These results suggest that the EEG and behavioral benefits of this NFB treatment persist for at least one year, which adds up to the available evidence contributing to identifying factors that increase its efficacy level. The relevance of this study lies in its prophylactic features of addressing a clinically healthy population with EEG risk of cognitive decline.

On the way home: a BCI-FES hand therapy self-managed by sub-acute SCI participants and their caregivers: a usability study.

BACKGROUND: Regaining hand function is the top priority for people with tetraplegia, however access to specialised therapy outwith clinics is limited. Here we present a system for hand therapy based on brain-computer interface (BCI) which uses a consumer grade electroencephalography (EEG) device combined with functional electrical stimulation (FES), and evaluate its usability among occupational therapists (OTs) and people with spinal cord injury (SCI) and their family members. METHODS: Users: Eight people with sub-acute SCI (6 M, 2F, age 55.4 ± 15.6) and their caregivers (3 M, 5F, age 45.3 ± 14.3); four OTs (4F, age 42.3 ± 9.8). User Activity: Researchers trained OTs; OTs subsequently taught caregivers to set up the system for the people with SCI to perform hand therapy. Hand therapy consisted of attempted movement (AM) of one hand to lower the power of EEG sensory-motor rhythm in the 8-12 Hz band and thereby activate FES which induced wrist flexion and extension. Technology: Consumer grade wearable EEG, multichannel FES, custom made BCI application. LOCATION: Research space within hospital. Evaluation: donning times, BCI accuracy, BCI and FES parameter repeatability, questionnaires, focus groups and interviews. RESULTS: Effectiveness: The BCI accuracy was 70-90%. Efficiency: Median donning times decreased from 40.5 min for initial session to 27 min during last training session (N = 7), dropping to 14 min on the last self-managed session (N = 3). BCI and FES parameters were stable from session to session. Satisfaction: Mean satisfaction with the system among SCI users and caregivers was 3.68 ± 0.81 (max 5) as measured by QUEST questionnaire. Main facilitators for implementing BCI-FES technology were "seeing hand moving", "doing something useful for the loved ones", good level of computer literacy (people with SCI and caregivers), "active engagement in therapy" (OT), while main barriers were technical complexity of setup (all groups) and "lack of clinical evidence" (OT). CONCLUSION: BCI-FES has potential to be used as at home hand therapy by people with SCI or stroke, provided it is easy to use and support is provided. Transfer of knowledge of operating BCI is possible from researchers to therapists to users and caregivers. Trial registration Registered with NHS GG&C on December 6th 2017; clinicaltrials.gov reference number NCT03257982, url: https://clinicaltrials.gov/ct2/show/NCT03257982 .

Responsive Neurostimulation as a Novel Palliative Option in Epilepsy Surgery.

Patients with drug-resistant focal onset epilepsy are not always suitable candidates for resective surgery, a definitive intervention to control their seizures. The alternative surgical treatment for these patients in Japan has been vagus nerve stimulation (VNS). Besides VNS, epileptologists in the United States can choose a novel palliative option called responsive neurostimulation (RNS), a closed-loop neuromodulation system approved by the US Food and Drug Administration in 2013. The RNS System continuously monitors neural electroencephalography (EEG) activity at the possible seizure onset zone (SOZ) where electrodes are placed and responds with electrical stimulation when a pre-defined epileptic activity is detected. The controlled clinical trials in the United States have demonstrated long-term utility and safety of the RNS System. Seizure reduction rates have continued to improve over time, reaching 75% over 9 years of treatment. The incidence of implant-site infection, the most frequent device-related adverse event, is similar to those of other neuromodulation devices. The RNS System has shown favorable efficacy for both mesial temporal lobe epilepsy (TLE) and neocortical epilepsy of the eloquent cortex. Another unique advantage of the RNS System is its ability to provide chronic monitoring of ambulatory electrocorticography (ECoG). Valuable information obtained from ECoG monitoring provides a better understanding of the state of epilepsy in each patient and improves clinical management. This article reviews the developmental history, structure, and clinical utility of the RNS System, and discusses its indications as a novel palliative option for drug-resistant epilepsy.

Recent Advancement of Technologies and the Transition to New Concepts in Epilepsy Surgery.

Fruitful progress and change have been accomplished in epilepsy surgery as science and technology advance. Stereotactic electroencephalography (SEEG) was originally developed by Talairach and Bancaud at Hôspital Sainte-Anne in the middle of the 20th century. SEEG has survived, and is now being recognized once again, especially with the development of neurosurgical robots. Many epilepsy centers have already replaced invasive monitoring with subdural electrodes (SDEs) by SEEG with depth electrodes worldwide. SEEG has advantages in terms of complication rates as shown in the previous reports. However, it would be more indispensable to demonstrate how much SEEG has contributed to improving seizure outcomes in epilepsy surgery. Vagus nerve stimulation (VNS) has been an only implantable device since 1990s, and has obtained the autostimulation mode which responds to ictal tachycardia. In addition to VNS, responsive neurostimulator (RNS) joined in the options of palliative treatment for medically refractory epilepsy. RNS is winning popularity in the United States because the device has abilities of both neurostimulation and recording of ambulatory electrocorticography (ECoG). Deep brain stimulation (DBS) has also attained approval as an adjunctive therapy in Europe and the United States. Ablative procedures such as SEEG-guided radiofrequency thermocoagulation (RF-TC) and laser interstitial thermal therapy (LITT) have been developed as less invasive options in epilepsy surgery. There will be more alternatives and tools in this field than ever before. Consequently, we will need to define benefits, indications, and limitations of these new technologies and concepts while adjusting ourselves to a period of fundamental transition in our foreseeable future.

A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings.

Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures.

A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter- and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter- and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram.

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectrum of EEG data related to motor imagery, the shared features across sessions or across subjects, namely, the mean and variance of model parameters, are extracted. Then, select the data sets that were most relevant to the new data set according to Euclidean distance to update the shared features. Finally, utilize the shared features and subject/session-specific features jointly to generate a new model. We evaluated our algorithm by analyzing the motor imagery EEG data from 10 healthy participants and a public data set from BCI competition IV. The classification accuracy of the proposed transfer learning is higher than that of traditional machine learning algorithms. The results of the paired t test showed that the classification results of PSD and the transfer learning algorithm were significantly different (p = 2.0946e-9), and the classification results of CSP and the transfer learning algorithm were significantly different (p = 1.9122e-6). The test accuracy of data set 2a of BCI competition IV was 85.7% ± 5.4%, which was higher than that of related traditional machine learning algorithms. Preliminary results suggested that the proposed algorithm can be effectively applied to the classification of motor imagery EEG signals across sessions and across subjects and the performance is better than that of the traditional machine learning algorithms. It can be promising to be applied to the field of brain-computer interface (BCI). Graphical abstract.

Utilizing High-Density Electroencephalography and Motion Capture Technology to Characterize Sensorimotor Integration While Performing Complex Actions.

Studies of sensorimotor integration often use sensory stimuli that require a simple motor response, such as a reach or a grasp. Recent advances in neural recording techniques, motion capture technologies, and time-synchronization methods enable studying sensorimotor integration using more complex sensory stimuli and performed actions. Here, we demonstrate that prehensile actions that require using complex sensory instructions for manipulating different objects can be characterized using high-density electroencephalography and motion capture systems. In 20 participants, we presented stimuli in different sensory modalities (visual, auditory) containing different contextual information about the object with which to interact. Neural signals recorded near motor cortex and posterior parietal cortex discharged based on both the instruction delivered and object manipulated. Additionally, kinematics of the wrist movements could be discriminated between participants. These findings demonstrate a proof-of-concept behavioral paradigm for studying sensorimotor integration of multidimensional sensory stimuli to perform complex movements. The designed framework will prove vital for studying neural control of movements in clinical populations in which sensorimotor integration is impaired due to information no longer being communicated correctly between brain regions (e.g. stroke). Such a framework is the first step towards developing a neural rehabilitative system for restoring function more effectively.

Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech.

OBJECTIVE: Measurement of the cortical tracking of continuous speech from electroencephalography (EEG) recordings using a forward model is an important tool in auditory neuroscience. Usually the stimulus is represented by its temporal envelope. Recently, the phonetic representation of speech was successfully introduced in English. We aim to show that the EEG prediction from phoneme-related speech features is possible in Dutch. The method requires a manual channel selection based on visual inspection or prior knowledge to obtain a summary measure of cortical tracking. We evaluate a method to (1) remove non-stimulus-related activity from the EEG signals to be predicted, and (2) automatically select the channels of interest. APPROACH: Eighteen participants listened to a Flemish story, while their EEG was recorded. Subject-specific and grand-average temporal response functions were determined between the EEG activity in different frequency bands and several stimulus features: the envelope, spectrogram, phonemes, phonetic features or a combination. The temporal response functions were used to predict EEG from the stimulus, and the predicted was compared with the recorded EEG, yielding a measure of cortical tracking of stimulus features. A spatial filter was calculated based on the generalized eigenvalue decomposition (GEVD), and the effect on EEG prediction accuracy was determined. MAIN RESULTS: A model including both low- and high-level speech representations was able to better predict the brain responses to the speech than a model only including low-level features. The inclusion of a GEVD-based spatial filter in the model increased the prediction accuracy of cortical responses to each speech feature at both single-subject (270% improvement) and group-level (310%). SIGNIFICANCE: We showed that the inclusion of acoustical and phonetic speech information and the addition of a data-driven spatial filter allow improved modelling of the relationship between the speech and its brain responses and offer an automatic channel selection.

Standardized music therapy with and without acclimatization, to improve EEG data acquisition in young children with and without disability.

INTRODUCTION: In young children, EEG data acquisition during stimulation tasks is difficult due to anxiety, movement and behaviorally-related interruptions, especially in those with disabilities. NEW METHOD: We used standardized music therapy (MT) protocols with and without acclimatization, during and prior to time-locked EEG with a published tactile testing protocol. Our prospective study leveraged a larger trial in children with/without cerebral palsy aged 7-27 months. Group1 received no preparation, Group2 received 15-minute MT prior to the EEG session, Group3 received the same as Group2 plus a rubber cap for home practice. All groups received MT procedural support during the EEG session. Sessions were stopped/started to acquire a full dataset. Trials were reviewed using a two-step artifact detection strategy by specialists masked to group allocation. RESULTS: 64 patients were included, 20 each in Groups 2 and 3, and 24 in Group1. Average age was 16.08 ± 6.33 months. All (100%) of children had data of sufficient quality and quantity for outcomes measurement without a second testing visit. There were no differences in useable trials by procedural group, disability status, age or stimulus condition. EEG recording time was shorter in Group3 vs. 1 (p = 0.02) and more patients in Group1 required repeat trials compared to Groups2 and 3 (p = 0.04 for both). COMPARISON WITH OLD METHOD: Our new methods resulted in no attrition from data loss, an improvement compared to published similar studies with data loss 30-55%. Acclimatization had minimal effects. CONCLUSION: In children under 3, MT protocols result in high rates of EEG data acquisition, decrease behaviorally-related interruptions and session acquisition time. This method is successful for typically developing children and those with cerebral palsy.

A Novel Approach for Classifying Native Chinese and Malay Speaking Persons According to Cortical Auditory Evoked Responses.

OBJECTIVES: This study uses a new approach for classifying the human ethnicity according to the auditory brain responses (electroencephalography [EEG] signals) with a high level of accuracy. Moreover, the study presents three different algorithms used to classify the human ethnicity using auditory brain responses. The algorithms were tested on Malays and Chinese as a case study. MATERIALS AND METHODS: The EEG signal was used as a brain response signal, which was evoked by two auditory stimuli (Tones and Consonant Vowels stimulus). The study was carried out on Malaysians (Malay and Chinese) with normal hearing and with hearing loss. A ranking process for the subjects' EEG data and the nonlinear features was used to obtain the maximum classification accuracy. RESULTS: The study formulated the classification of Normal Hearing Ethnicity Index and Sensorineural Hearing Loss Ethnicity Index. These indices classified the human ethnicity according to brain auditory responses by using numerical values of response signal features. Three classification algorithms were used to verify the human ethnicity. Support Vector Machine (SVM) classified the human ethnicity with an accuracy of 90% in the cases of normal hearing and sensorineural hearing loss (SNHL); the SVM classified with an accuracy of 84%. CONCLUSION: The classification indices categorized or separated the human ethnicity in both hearing cases of normal hearing and SNHL with high accuracy. The SVM classifier provided a good accuracy in the classification of the auditory brain responses. The proposed indices might constitute valuable tools for the classification of the brain responses according to the human ethnicity.

Frequency following responses to tone glides: Effects of frequency extent, direction, and electrode montage.

The spectral (frequency) and amplitude cues in speech change rapidly over time. Study of the neural encoding of these dynamic features may help to improve diagnosis and treatment of speech-perception difficulties. This study uses tone glides as a simple approximation of dynamic speech sounds to better our understanding of the underlying neural representation of speech. The frequency following response (FFR) was recorded from 10 young normal-hearing adults using six signals varying in glide direction (rising and falling) and extent of frequency change (13, 23, and 1 octave). In addition, the FFR was simultaneously recorded using two different electrode montages (vertical and horizontal). These factors were analyzed across three time windows using a measure of response strength (signal-to-noise ratio) and a measure of temporal coherence (stimulus-to-response correlation coefficient). Results demonstrated effects of extent, montage, and a montage-by-window interaction. SNR and stimulus-to-response correlation measures differed in their sensitivity to these factors. These results suggest that the FFR reflects dynamic acoustic characteristics of simple tonal stimuli very well. Additional research is needed to determine how neural encoding may differ for more natural dynamic speech signals and populations with impaired auditory processing.

WaveCSP: a robust motor imagery classifier for consumer EEG devices.

There is an increasing demand for reliable motor imagery (MI) classification algorithms for applications in consumer level brain-computer interfacing (BCI). For the practical use, such algorithms must be robust to both device limitations and subject variability, which make MI classification a challenging task. This study proposes methods to study the effect of limitations including a limited number of electrodes, limited spatial distribution of electrodes, lower signal quality, subject variabilities and BCI literacy, on the performance of MI classification. To mitigate these limitations, we propose a machine learning approach, WaveCSP that uses 24 features extracted from EEG signals using wavelet transform and common spatial pattern (CSP) filtering techniques. The algorithm shows better performance in terms of subject variability compared to existing work. The application of WaveCSP to Physionet MI database shows more than 50% of the 109 subjects achieving accuracy higher than 64%. The data obtained from a commercial EEG headset using the same experimental protocol result in up to four out of five subjects who had prior BCI experience (out of a total of 25 subjects) performing with accuracy higher than 64%.

Prospects of Synchronous fMRI-EEG Recording as the Basis for Neurofeedback (Exemplified on Patient with Stroke Sequelae).

Synchronous fMRI-EEG mapping of cerebral activity in stroke patients made it possible to implement neurofeedback, a novel and promising therapeutic technology. This method integrates a real-time monitoring of cerebral activity by EEG and fMRI signals and training of the patients to control this activity simultaneously or alternatively via neurofeedback. The targets of such cerebral stimulation are cortical regions controlling arbitrary movements (Brodmann area 4), whereas its aim is optimization of activity in these regions in order to achieve better rehabilitation of stroke patients. The paper discusses the methodical details, advantages, and promise of bimodal neurofeedback treatment.

Dynamics of fMRI and EEG Parameters in a Stroke Patient Assessed during a Neurofeedback Course Focused on Brodmann Area 4 (M1).

A course of interactive stimulation of primary motor cortex (Brodmann area 4) in the brain of a stroke patient resulted in recovery of locomotion volume in the paretic extremities and in improvement of general health accompanied with diverse changes in cerebral activity. During the training course, the magnitude of response in the visual fields of Brodmann areas 17 and 18 decreased; in parallel, the motor areas were supplemented with other ones such as area 24 (the ventral surface of anterior cingulate gyrus responsible for self-regulation of human brain activity and implicated into synthesis of tactile and special information) in company with Brodmann areas 40, 41, 43, 44, and 45. EEG data showed that neurofeedback sessions persistently increased the θ rhythm power in Brodmann areas 7, 39, 40, and 47, while the corresponding powers progressively decreased during a real motion. Both real motion and its virtual sibling constructed by interactive stimulation via neurofeedback were characterized with decreasing powers of the EEG ß rhythm in Brodmann areas 6 and 8. The neurofeedback course decreased the coherence between the left Brodmann area 6 and some other ones examined in α and θ ranges. In the context of real motions, the coherence assessed in the EEG ß range generally increased. Overall, the EEG and fMRI parameters attest to growing similarity between the moieties of functional communications effected in real and imaginary movements during neurofeedback course. The data open the vista for interactive stimulation to rehabilitate stroke patients; they highlight the important role of Brodmann areas in rearrangement of the brain in such patients; finally, the present results revealed the "common nervous pathway" that can be used to restore the capability for imaginary and real movements by a neurofeedback course after stroke.

Cloud computing for seizure detection in implanted neural devices.

OBJECTIVE: Closed-loop implantable neural stimulators are an exciting treatment option for patients with medically refractory epilepsy, with a number of new devices in or nearing clinical trials. These devices must accurately detect a variety of seizure types in order to reliably deliver therapeutic stimulation. While effective, broadly-applicable seizure detection algorithms have recently been published, these methods are too computationally intensive to be directly deployed in an implantable device. We demonstrate a strategy that couples devices to cloud computing resources in order to implement complex seizure detection methods on an implantable device platform. APPROACH: We use a sensitive gating algorithm capable of running on-board a device to identify potential seizure epochs and transmit these epochs to a cloud-based analysis platform. A precise seizure detection algorithm is then applied to the candidate epochs, leveraging cloud computing resources for accurate seizure event detection. This seizure detection strategy was developed and tested on eleven human implanted device recordings generated using the NeuroVista Seizure Advisory System. MAIN RESULTS: The gating algorithm achieved high-sensitivity detection using a small feature set as input to a linear classifier, compatible with the computational capability of next-generation implantable devices. The cloud-based precision algorithm successfully identified all seizures transmitted by the gating algorithm while significantly reducing the false positive rate. Across all subjects, this joint approach detected 99% of seizures with a false positive rate of 0.03 h-1. SIGNIFICANCE: We present a novel framework for implementing computationally intensive algorithms on human data recorded from an implanted device. By using telemetry to intelligently access cloud-based computational resources, the next generation of neuro-implantable devices will leverage sophisticated algorithms with potential to greatly improve device performance and patient outcomes.

Effects of an Integrated Neurofeedback System with Dry Electrodes: EEG Acquisition and Cognition Assessment.

Electroencephalogram (EEG) neurofeedback improves cognitive capacity and behaviors by regulating brain activity, which can lead to cognitive enhancement in healthy people and better rehabilitation in patients. The increased use of EEG neurofeedback highlights the urgent need to reduce the discomfort and preparation time and increase the stability and simplicity of the system's operation. Based on brain-computer interface technology and a multithreading design, we describe a neurofeedback system with an integrated design that incorporates wearable, multichannel, dry electrode EEG acquisition equipment and cognitive function assessment. Then, we evaluated the effectiveness of the system in a single-blind control experiment in healthy people, who increased the alpha frequency band power in a neurofeedback protocol. We found that upregulation of the alpha power density improved working memory following short-term training (only five training sessions in a week), while the attention network regulation may be related to other frequency band activities, such as theta and beta. Our integrated system will be an effective neurofeedback training and cognitive function assessment system for personal and clinical use.

Towards optimal visual presentation design for hybrid EEG-fTCD brain-computer interfaces.

OBJECTIVE: In this paper, we introduce a novel hybrid brain-computer interface (BCI) system that measures electrical brain activity as well as cerebral blood velocity using electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) respectively in response to flickering mental rotation (MR) and flickering word generation (WG) cognitive tasks as well as a fixation cross that represents the baseline. This work extends our previous approach, in which we showed that motor imagery induces simultaneous changes in EEG and fTCD to enable task discrimination; and hence, provides a design approach for a hybrid BCI. Here, we show that instead of using motor imagery, the proposed visual stimulation technique enables the design of an EEG-fTCD based BCI with higher accuracy. APPROACH: Features based on the power spectrum of EEG and fTCD signals were calculated. Mutual information and support vector machines were used for feature selection and classification purposes. MAIN RESULTS: EEG-fTCD combination outperformed EEG by 4.05% accuracy for MR versus baseline problem and by 5.81% accuracy for WG versus baseline problem. An average accuracy of 92.38% was achieved for MR versus WG problem using the hybrid combination. Average transmission rates of 4.39, 3.92, and 5.60 bits min-1 were obtained for MR versus baseline, WG versus baseline, and MR versus WG problems respectively. SIGNIFICANCE: In terms of accuracy, the current visual presentation outperforms the motor imagery visual presentation we designed before for the EEG-fTCD system by 10% accuracy for task versus task problem. Moreover, the proposed system outperforms the state of the art hybrid EEG-fNIRS BCIs in terms of accuracy and/or information transfer rate. Even though there are still limitations of the proposed system, such promising results show that the proposed hybrid system is a feasible candidate for real-time BCIs.

A ZigBee-based wireless system for monitoring vital signs in hyperbaric chambers: Technical report.

This paper presents the replacement of a traditional wired communication link of the hyperbaric chambers with a wireless ZigBee-based system. This move allows a reduction in the costs of seals capable of withstanding the internal pressures and gives rise to a more versatile system. The new system is able to capture and process individual vital signs like the electrocardiography signal, and other analog sources, sending the data to an external computer and allowing analysis, representation and sharing with medical staff. This system solves such problems as the attenuation of the signal produced by the metal walls of the hyperbaric chamber and has a coverage area large enough to manage up to six patients with an effective data rate conversion of 2kHz. Furthermore, a battery-based and multiparameter platform is designed for multipatient hyperbaric chambers.