A brain-computer interface based on bilateral transcranial Doppler ultrasound

Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease

Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.

Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review

Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population. A systematic review was conducted using the Scopus, PubMed, and Ovid Medline databases. Studies of children and adolescents that reported BCI performance published in English in peer-reviewed journals between 2008 and May 2020 were included. Twelve publications were identified, providing strong evidence for continued research in pediatric BCIs. Research evidence was generally at multiple case study or exploratory study level, with modest sample sizes. Seven studies focused on BCIs for communication and five on mobility. Articles were categorized and grouped based on type of measurement (i.e., non-invasive and invasive), and the type of brain signal (i.e., sensory evoked potentials or movement-related potentials). Strengths and limitations of studies were identified and used to provide requirements for clinical translation of pediatric BCIs. This systematic review presents the state-of-the-art of pediatric BCIs focused on developing advanced technology to support children and youth with communication disabilities or limited manual ability. Despite a few research studies addressing the application of BCIs for communication and mobility in children, results are encouraging and future works should focus on customizable pediatric access technologies based on brain activity.

Sensor Modalities for Brain-Computer Interface Technology: A Comprehensive Literature Review

Brain-computer interface (BCI) technology is rapidly developing and changing the paradigm of neurorestoration by linking cortical activity with control of an external effector to provide patients with tangible improvements in their ability to interact with the environment. The sensor component of a BCI circuit dictates the resolution of brain pattern recognition and therefore plays an integral role in the technology. Several sensor modalities are currently in use for BCI applications and are broadly either electrode-based or functional neuroimaging-based. Sensors vary in their inherent spatial and temporal resolutions, as well as in practical aspects such as invasiveness, portability, and maintenance. Hybrid BCI systems with multimodal sensory inputs represent a promising development in the field allowing for complimentary function. Artificial intelligence and deep learning algorithms have been applied to BCI systems to achieve faster and more accurate classifications of sensory input and improve user performance in various tasks. Neurofeedback is an important advancement in the field that has been implemented in several types of BCI systems by showing users a real-time display of their recorded brain activity during a task to facilitate their control over their own cortical activity. In this way, neurofeedback has improved BCI classification and enhanced user control over BCI output. Taken together, BCI systems have progressed significantly in recent years in terms of accuracy, speed, and communication. Understanding the sensory components of a BCI is essential for neurosurgeons and clinicians as they help advance this technology in the clinical setting.

Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface

Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

Numerical Evaluation of the Influence of Skull Heterogeneity on Transcranial Ultrasonic Focusing

In transcranial penetration, ultrasound undergoes refraction, diffraction, multi-reflection, and mode conversion. These factors lead to phase aberration and waveform distortion, which impede the realization of transcranial ultrasonic imaging and therapy. Ray tracing has been used to correct the phase aberration and is computationally more efficient than traditional full-wave simulation. However, when ray tracing has been used for transcranial investigation, it has generally been on the premise that the skull medium is homogeneous. To find suitable homogeneity that balances computational speed and accuracy, the present work investigates how the focus deviates after phase-aberration compensation with ray tracing using time-reversal theory. The waveforms are synthetized with ray tracing for phase aberration, by which the properties of the skull bone are simplified for refraction calculation as those of either (i) the cortical bone or (ii) the mean of the entire skull bone, and the focusing accuracy is evaluated for each hypothesis. The propagation of ultrasound for transcranial focusing is simulated with the elastic model using the k-space pseudospectral method. Unlike the fluid model, the elastic model does not omit shear waves in the skull bones, and the influence of that omission is investigated, with the fluid model resulting in a focal deflection of 0.5 mm. The focusing deviations are huge when the properties of the skull bone are idealized with ray tracing as those of the mean of the entire skull bone. The focusing accuracy improves when the properties of the skull bone are idealized as those of the cortical bone. The results reveal minimal deviation (8.6, 3.9, and 3.2% in the three Cartesian coordinates) in the focal region and suggest that transcranial focusing deflections are caused mostly by ultrasonic refraction on the surface of the skull bone. A heterogeneous skull bone causes wave bending but minimal focusing deflection. The proposed simplification of a homogeneous skull bone is more accurate for transcranial ultrasonic path estimation and offers promising applications in transcranial ultrasonic focusing and imaging.

EEG-fTCD hybrid brain-computer interface using template matching and wavelet decomposition

OBJECTIVE

We aim at developing a hybrid brain-computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and cerebral blood velocity respectively in response to flickering mental rotation (MR) and word generation (WG) tasks. In this paper, we improve both the accuracy and information transfer rate (ITR) of this novel hybrid brain computer interface (BCI) we designed in our previous work.

APPROACH

To achieve such aim, we extended our feature extraction approach through using template matching and multi-scale analysis to extract EEG and fTCD features, respectively. In particular, template matching was used to analyze EEG data whereas 5-level wavelet decomposition was applied to fTCD data. Significant EEG and fTCD features were selected using Wilcoxon signed rank test. Support vector machines classifier (SVM) was used to project EEG and fTCD selected features of each trial into scalar SVM scores. Moreover, instead of concatenating EEG and fTCD feature vectors corresponding to each trial, we proposed a Bayesian fusion approach of EEG and fTCD evidences.

MAIN RESULTS

Average accuracy and average ITR of 98.11% and 21.29 bits min^-1 were achieved for WG versus MR classification while MR versus baseline yielded 86.27% average accuracy and 8.95 bit min^-1 average ITR. In addition, average accuracy of 85.29% and average ITR of 8.34 bits min^-1 were obtained for WG versus baseline.

SIGNIFICANCE

The proposed analysis techniques significantly improved the hybrid BCI performance. Specifically, for MR/WG versus baseline problems, we achieved twice of the ITRs obtained in our previous study. Moreover, the ITR of WG versus MR problem is 4-times the ITR we obtained before for the same problem. The current analysis methods boosted the performance of our EEG-fTCD BCI such that it outperformed the existing EEG-fNIRS BCIs in comparison.

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.

Design and Validation of an FPGA-Based Configurable Transcranial Doppler Neurofeedback System for Chronic Pain Patients

Neurofeedback is a self-regulation technique that can be applied to learn to voluntarily control cerebral activity in specific brain regions. In this work, a Transcranial Doppler-based configurable neurofeedback system is proposed and described. The hardware configuration is based on the Red Pitaya board, which gives great flexibility and processing power to the system. The parameter to be trained can be selected between several temporal, spectral, or complexity features from the cerebral blood flow velocity signal in different vessels. As previous studies have found alterations in these parameters in chronic pain patients, the system could be applied to help them to voluntarily control these parameters. Two protocols based on different temporal lengths of the training periods have been proposed and tested with six healthy subjects that were randomly assigned to one of the protocols at the beginning of the procedure. For the purposes of the testing, the trained parameter was the mean cerebral blood flow velocity in the aggregated data from the two anterior cerebral arteries. Results show that, using the proposed neurofeedback system, the two groups of healthy volunteers can learn to self-regulate a parameter from their brain activity in a reduced number of training sessions.

Altered cerebral blood flow velocity features in fibromyalgia patients in resting-state conditions

The aim of this study is to characterize in resting-state conditions the cerebral blood flow velocity (CBFV) signals of fibromyalgia patients. The anterior and middle cerebral arteries of both hemispheres from 15 women with fibromyalgia and 15 healthy women were monitored using Transcranial Doppler (TCD) during a 5-minute eyes-closed resting period. Several signal processing methods based on time, information theory, frequency and time-frequency analyses were used in order to extract different features to characterize the CBFV signals in the different vessels. Main results indicated that, in comparison with control subjects, fibromyalgia patients showed a higher complexity of the envelope CBFV and a different distribution of the power spectral density. In addition, it has been observed that complexity and spectral features show correlations with clinical pain parameters and emotional factors. The characterization features were used in a lineal model to discriminate between fibromyalgia patients and healthy controls, providing a high accuracy. These findings indicate that CBFV signals, specifically their complexity and spectral characteristics, contain information that may be relevant for the assessment of fibromyalgia patients in resting-state conditions.

An emergency call system for patients in locked-in state using an SSVEP-based brain switch

Patients in a locked-in state (LIS) due to severe neurological disorders such as amyotrophic lateral sclerosis (ALS) require seamless emergency care by their caregivers or guardians. However, it is a difficult job for the guardians to continuously monitor the patients' state, especially when direct communication is not possible. In the present study, we developed an emergency call system for such patients using a steady-state visual evoked potential (SSVEP)-based brain switch. Although there have been previous studies to implement SSVEP-based brain switch system, they have not been applied to patients in LIS, and thus their clinical value has not been validated. In this study, we verified whether the SSVEP-based brain switch system can be practically used as an emergency call system for patients in LIS. The brain switch used for our system adopted a chromatic visual stimulus, which proved to be visually less stimulating than conventional checkerboard-type stimuli but could generate SSVEP responses strong enough to be used for brain-computer interface (BCI) applications. To verify the feasibility of our emergency call system, 14 healthy participants and 3 patients with severe ALS took part in online experiments. All three ALS patients successfully called their guardians to their bedsides in about 6.56 seconds. Furthermore, additional experiments with one of these patients demonstrated that our emergency call system maintains fairly good performance even up to 4 weeks after the first experiment without renewing initial calibration data. Our results suggest that our SSVEP-based emergency call system might be successfully used in practical scenarios.

An online three-class Transcranial Doppler ultrasound brain computer interface

Brain computer interfaces (BCI) can provide communication opportunities for individuals with severe motor disabilities. Transcranial Doppler ultrasound (TCD) measures cerebral blood flow velocities and can be used to develop a BCI. A previously implemented TCD BCI system used verbal and spatial tasks as control signals; however, the spatial task involved a visual cue that awkwardly diverted the user's attention away from the communication interface. Therefore, vision-independent right-lateralized tasks were investigated. Using a bilateral TCD BCI, ten participants controlled online, an on-screen keyboard using a left-lateralized task (verbal fluency), a right-lateralized task (fist motor imagery or 3D-shape tracing), and unconstrained rest. 3D-shape tracing was generally more discernible from other tasks than was fist motor imagery. Verbal fluency, 3D-shape tracing and unconstrained rest were distinguished from each other using a linear discriminant classifier, achieving a mean agreement of κ=0.43±0.17. These rates are comparable to the best offline three-class TCD BCI accuracies reported thus far. The online communication system achieved a mean information transfer rate (ITR) of 1.08±0.69bits/min with values reaching up to 2.46bits/min, thereby exceeding the ITR of previous online TCD BCIs. These findings demonstrate the potential of a three-class online TCD BCI that does not require visual task cues.

The Evolution of Neuroprosthetic Interfaces

The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.

Single-Trial Analysis of Inter-Beat Interval Perturbations Accompanying Single-Switch Scanning: Case Series of Three Children With Severe Spastic Quadriplegic Cerebral Palsy

Single-switch access in conjunction with scanning remains a fundamental solution in restoring communication for many children with profound physical disabilities. However, untimely switch inaction and unintentional switch activations can lead to user frustration and impede functional communication. A previous preliminary study, in the context of a case series with three single-switch users, reported that correct, accidental and missed switch activations could elicit cardiac deceleration and increased phasic skin conductance on average, while deliberate switch non-use was associated with autonomic nonresponse. The present study investigated the possibility of using blood volume pulse recordings from the same three pediatric single-switch users to track the aforementioned switch events on a single-trial basis. Peaks of the line length time series derived from the empirical mode decomposition of the inter-beat interval time series matched, on average, a high percentage (above 80%) of single-switch events, while unmatched peaks coincided moderately (below 37%) with idle time during scanning. These results encourage further study of autonomic measures as complementary information channels to enhance single-switch access.

Effects of user mental state on EEG-BCI performance

Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this study, we investigated the effects of three mental states—fatigue, frustration, and attention—on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the performance of user-specific mental tasks. Following training, participants completed three testing sessions, during which they used the BCI to play a simple maze navigation game while periodically reporting their perceived levels of fatigue, frustration, and attention. Statistical analysis indicated that there is a significant relationship between frustration and BCI performance while the relationship between fatigue and BCI performance approached significance. BCI performance was 7% lower than average when self-reported fatigue was low and 7% higher than average when self-reported frustration was moderate. A multivariate analysis of mental state revealed the presence of contiguous regions in mental state space where BCI performance was more accurate than average, suggesting the importance of moderate fatigue for achieving effortless focus on BCI control, frustration as a potential motivating factor, and attention as a compensatory mechanism to increasing frustration. Finally, a visual analysis showed the sensitivity of underlying class distributions to changes in mental state. Collectively, these results indicate that mental state is closely related to BCI performance, encouraging future development of psychologically adaptive BCIs.

Comparing methods for determining motor-hand lateralization based on fTCD signals

The lateralization index (LI) as determined from functional transcranial Doppler sonography (fTCD) can be used to determine the hemispheric organization of neural activation during a behavioral task. Previous studies have proposed different methods to determine this index, but to our knowledge no studies have compared the performance of these methods. In this study, we compare two established methods with a simpler method proposed here. The aim was to see whether similar results could be achieved with a simpler method and to give an indication of the analysis steps required to determine the LI. A simple unimanual motor task was performed while fTCD was acquired, and the LI determined by each of these methods was compared. In addition, LI determined by each method was related to behavioural output in the form of degree of handedness. The results suggest that although the methods differed in complexity, they yielded similar results when determining the lateralization of motor functions, and its correlation with behavior. Further investigation is needed to expand the conclusions of this preliminary study, however the new method proposed in the paper has great potential as it is much simpler than the more established methods yet yields similar results.

Online transcranial Doppler ultrasonographic control of an onscreen keyboard

Brain-computer interface (BCI) systems exploit brain activity for generating a control command and may be used by individuals with severe motor disabilities as an alternative means of communication. An emerging brain monitoring modality for BCI development is transcranial Doppler ultrasonography (TCD), which facilitates the tracking of cerebral blood flow velocities associated with mental tasks. However, TCD-BCI studies to date have exclusively been offline. The feasibility of a TCD-based BCI system hinges on its online performance. In this paper, an online TCD-BCI system was implemented, bilaterally tracking blood flow velocities in the middle cerebral arteries for system-paced control of a scanning keyboard. Target letters or words were selected by repetitively rehearsing the spelling while imagining the writing of the intended word, a left-lateralized task. Undesired letters or words were bypassed by performing visual tracking, a non-lateralized task. The keyboard scanning period was 15 s. With 10 able-bodied right-handed young adults, the two mental tasks were differentiated online using a Naïve Bayes classification algorithm and a set of time-domain, user-dependent features. The system achieved an average specificity and sensitivity of 81.44 ± 8.35 and 82.30 ± 7.39%, respectively. The level of agreement between the intended and machine-predicted selections was moderate (κ = 0.60). The average information transfer rate was 0.87 bits/min with an average throughput of 0.31 ± 0.12 character/min. These findings suggest that an online TCD-BCI can achieve reasonable accuracies with an intuitive language task, but with modest throughput. Future interface and signal classification enhancements are required to improve communication rate.

Assessment of resting-state blood flow through anterior cerebral arteries using trans-cranial doppler recordings

Trans-cranial Doppler (TCD) recordings are used to monitor cerebral blood flow in the main cerebral arteries. The resting state is usually characterized by the mean velocity or the maximum Doppler shift frequency (an envelope signal) by insonating the middle cerebral arteries. In this study, we characterized cerebral blood flow in the anterior cerebral arteries. We analyzed both envelope signals and raw signals obtained from bilateral insonation. We recruited 20 healthy patients and conducted the data acquisition for 15 min. Features were extracted from the time domain, the frequency domain and the time-frequency domain. The results indicate that a gender-based statistical difference exists in the frequency and time-frequency domains. However, no handedness effect was found. In the time domain, information-theoretic features indicated that mutual dependence is higher in raw signals than in envelope signals. Finally, we concluded that insonation of the anterior cerebral arteries serves as a complement to middle cerebral artery studies. Additionally, investigation of the raw signals provided us with additional information that is not otherwise available from envelope signals. Use of direct trans-cranial Doppler raw data is therefore validated as a valuable method for characterizing the resting state.

Towards a multimodal brain-computer interface: combining fNIRS and fTCD measurements to enable higher classification accuracy

Previous brain-computer interface (BCI) research has largely focused on single neuroimaging modalities such as near-infrared spectroscopy (NIRS) or transcranial Doppler ultrasonography (TCD). However, multimodal brain-computer interfaces, which combine signals from different brain modalities, have been suggested as a potential means of improving the accuracy of BCI systems. In this paper, we compare the classification accuracies attainable using NIRS signals alone, TCD signals alone, and a combination of NIRS and TCD signals. Nine able-bodied subjects (mean age=25.7) were recruited and simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. Using Linear Discriminant Analysis, the verbal fluency task was classified at mean accuracies of 76.1±9.9%, 79.4±10.3%, and 86.5±6.0% using NIRS, TCD, and NIRS-TCD systems respectively. In five of nine participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising method of improving the accuracy of future brain-computer interfaces.

An analysis of resting-state functional transcranial Doppler recordings from middle cerebral arteries

Functional transcrannial Doppler (fTCD) is used for monitoring the hemodynamics characteristics of major cerebral arteries. Its resting-state characteristics are known only when considering the maximal velocity corresponding to the highest Doppler shift (so called the envelope signals). Significantly more information about the resting-state fTCD can be gained when considering the raw cerebral blood flow velocity (CBFV) recordings. In this paper, we considered simultaneously acquired envelope and raw CBFV signals. Specifically, we collected bilateral CBFV recordings from left and right middle cerebral arteries using 20 healthy subjects (10 females). The data collection lasted for 15 minutes. The subjects were asked to remain awake, stay silent, and try to remain thought-free during the data collection. Time, frequency and time-frequency features were extracted from both the raw and the envelope CBFV signals. The effects of age, sex and body-mass index were examined on the extracted features. The results showed that the raw CBFV signals had a higher frequency content, and its temporal structures were almost uncorrelated. The information-theoretic features showed that the raw recordings from left and right middle cerebral arteries had higher content of mutual information than the envelope signals. Age and body-mass index did not have statistically significant effects on the extracted features. Sex-based differences were observed in all three domains and for both, the envelope signals and the raw CBFV signals. These findings indicate that the raw CBFV signals provide valuable information about the cerebral blood flow which can be utilized in further validation of fTCD as a clinical tool.

Towards a hemodynamic BCI using transcranial Doppler without user-specific training data

Transcranial Doppler (TCD) was recently introduced as a new brain-computer interface (BCI) modality for detecting task-induced hemispheric lateralization. To date, single-trial discrimination between a lateralized mental activity and a rest state has been demonstrated with long (45 s) activation time periods. However, the possibility of detecting successive activations in a user-independent framework (i.e. without training data from the user) remains an open question.

OBJECTIVE

The objective of this research was to assess TCD-based detection of lateralized mental activity with a user-independent classifier. In so doing, we also investigated the accuracy of detecting successive lateralizations. Approach. TCD data from 18 participants were collected during verbal fluency, mental rotation tasks and baseline counting tasks. Linear discriminant analysis and a set of four time-domain features were used to classify successive left and right brain activations.

MAIN RESULTS

In a user-independent framework, accuracies up to 74.6 ± 12.6% were achieved using training data from a single participant, and lateralization task durations of 18 s.

SIGNIFICANCE

Subject-independent, algorithmic classification of TCD signals corresponding to successive brain lateralization may be a feasible paradigm for TCD-BCI design.

Towards increased data transmission rate for a three-class metabolic brain-computer interface based on transcranial Doppler ultrasound

In this study, we conducted an offline analysis of transcranial Doppler (TCD) ultrasound recordings to investigate potential methods for increasing data transmission rate in a TCD-based brain-computer interface. Cerebral blood flow velocity was recorded within the left and right middle cerebral arteries while nine able-bodied participants alternated between rest and two different mental activities (word generation and mental rotation). We differentiated these three states using a three-class linear discriminant analysis classifier while the duration of each state was varied between 5 and 30s. Maximum classification accuracies exceeded 70%, and data transmission rate was maximized at 1.2 bits per minute, representing a four-fold increase in data transmission rate over previous two-class analysis of TCD recordings.

Classification of activity engagement in individuals with severe physical disabilities using signals of the peripheral nervous system

Communication barriers often result in exclusion of children and youth with disabilities from activities and social settings that are essential to their psychosocial development. In particular, difficulties in describing their experiences of activities and social settings hinder our understanding of the factors that promote inclusion and participation of this group of individuals. To address this specific communication challenge, we examined the feasibility of developing a language-free measure of experience in youth with severe physical disabilities. To do this, we used the activity of the peripheral nervous system to detect patterns of psychological arousal associated with activities requiring different patterns of cognitive/affective and interpersonal involvement (activity engagement). We demonstrated that these signals can differentiate among patterns of arousal associated with these activities with high accuracy (two levels: 81%, three levels: 74%). These results demonstrate the potential for development of a real-time, motor- and language-free measure for describing the experiences of children and youth with disabilities.