A5021991448- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Gaze Tracking and Assistive Technology
- Neural dynamics and brain function
- Neural and Behavioral Psychology Studies
- Advanced Memory and Neural Computing
- Blind Source Separation Techniques
- Mindfulness and Compassion Interventions
- Heart Rate Variability and Autonomic Control
- PAPR reduction in OFDM
- Pediatric Pain Management Techniques
- Advanced Wireless Communication Techniques
- Functional Brain Connectivity Studies
- ECG Monitoring and Analysis
- RNA and protein synthesis mechanisms
- Pain Mechanisms and Treatments
- Gene expression and cancer classification
- Music Therapy and Health
- Emotion and Mood Recognition
- Genomics and Phylogenetic Studies
- Wireless Communication Networks Research
- Airway Management and Intubation Techniques
- Power Line Communications and Noise
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Northeastern University
2010-2021
Genapsys (United States)
2019
Oregon Health & Science University
2014
Universidad del Noreste
2011-2013
This study investigates measures of mindfulness meditation (MM) as a mental practice, in which resting but alert state mind is maintained. A population older people with high stress level participated this study, while electroencephalographic (EEG) and respiration signals were recorded during MM intervention. The physiological control conditions analyzed signal processing. EEG data collected on 34 novice meditators after 6-week Collected spectral analysis, phase analysis classification to...
Objective pain assessment is required for appropriate management in the clinical setting. However, gold standard based on subjective methods. Automated detection from physiological data may provide important objective information to better standardize assessment. Specifically, electrodermal activity (EDA) can identify features of stress and anxiety induced by varying levels. notable variability EDA measurement exists research date has demonstrated sensitivity but lack specificity In this...
Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. Specifically, EEG-based BCI typing systems, different symbol presentation paradigms have been utilized to induce ERPs. In this manuscript, through an experimental study, we assess the speed, recorded signal quality, and system accuracy of a language-model-assisted using three paradigms: 4 × 7 matrix paradigm 28-character alphabet with...
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary deliberate. The presented approach supplements the self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions develop an objective metric. Such is specifically...
Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used detect the intent of person for brain computer interfaces (BCI). This paradigm is widely utilized build letter-by-letter text input systems using BCI. Nevertheless BCI-typewriter depending only on EEG responses will not sufficiently accurate single-trial operation general, and existing utilize many-trial schemes achieve accuracy at cost speed. Hence incorporation language model based prior...
Mindfulness meditation (MM) is an inward mental practice, in which a resting but alert state of mind maintained. MM intervention was performed for population older people with high stress levels. This study assessed signal processing methodologies electroencephalographic (EEG) and respiration signals during control condition to aid quantification the meditative state. EEG data were collected analyzed on 34 novice meditators after 6-week intervention. Collected spectral analysis support...
Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a relevant instruction, from brain symbols, classifiable physiological states. For example, in motor imagery robot control user would indicate their choice dictionary of symbols (rotate arm left, grasp, etc.) by selecting smaller (imagined left or right hand movements). We examine how BCI infers symbol using selections symbols. offer recursive Bayesian decision framework which incorporates context prior distributions (e.g.,...
Abstract Electroencephalography (EEG) signals have been an attractive choice to build noninvasive brain computer interfaces (BCIs) for nearly three decades. Depending on the stimuli, there are different responses which one could get from EEG signals. One of them is P300 response a visually evoked that has widely studied. Steady state potential (SSVEP) oscillating stimulus with fixed frequency, detectable visual cortex. However, exists some work using m‐sequence lags as control sequence...
Brain computer interfaces (BCIs) offer individuals with disabilities an alternative channel of communication and control, hence they have been receiving increasing interest. BCIs can also be useful for healthy in situations limiting their movement or where other interaction modalities need to supplemented. Event-related steady state visually evoked potentials (SSVEPs) are the top two brain signal types used developing that allow user make a choice from discrete set options, including...
Visually evoked potentials have attracted great attention in the last two decades for purpose of brain computer interface design. P300 response is a major signal interest that has been widely studied. Steady state visual occur to periodically flickering stimuli primarily investigated as an alternative. There also exists some work on use m-sequence and its shifted versions induce responses are cortex but not periodic. In this paper, we study multiple m-sequences intent discrimination...
A simulation framework could decrease the burden of attending long and tiring experimental sessions on potential users brain computer interface (BCI) systems. Specifically during initial design a BCI, that replicate operational performance system would be useful tool for designers to make choices. In this manuscript, we develop Monte Carlo based probabilistic electroencephalography (EEG) BCI design. We employ one event related (ERP) typing steady state evoked (SSVEP) control as testbeds....
A class of brain computer interfaces (BCIs) employs noninvasive recordings electroencephalography (EEG) signals to enable users with severe speech and motor impairments interact their environment social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) inference. Presentation paradigm design in current ERP-based letter by typically query the user an arbitrary subset characters. However, accuracy also speed can potentially be enhanced more...
Steady state visual evoked potentials are widely exploited in EEG-based BCI systems. Frequency and code based flickering stimuli the two major methods used to induce SSVEP responses. Considering tiring effect of flashing icons long run, less noticeable flashes become, more tolerable they will be. Based on user ratings, who experienced both, frequency stimulation, method is tiring. Hence, we our system code-based mode for this study. Among several aspects affecting performance experience,...
Currently, many Brain Computer Interfaces (BCI) classifiers output point estimates of user intent which make it difficult to incorporate context prior information or assign a principled confidence measurement decision. We propose Bayesian framework extend current Steady State Visually Evoked Potential (SSVEP) maximum posteriori (MAP) by using Kernel Density Estimate (KDE) learn the distribution features conditioned on stimulation class. To demonstrate our we Canonical Correlation Analysis...
This paper describes a capstone design project by four undergraduate students (first authors listed in alphabetical order last name). A noninvasive brain computer interface (BCI) based on the steady state visual evoked potential (SSVEP) has been developed and utilized controlling an iRobot platform remotely real-time closed-loop fashion using video feedback from robot's eye view to operator over internet. The selects commands focusing gaze one of flickering checkerboards surrounding window...
AbstractOne of the principal application areas for brain-computer interface (BCI) technology is augmentative and alternative communication (AAC), typically used by people with severe speech physical disabilities (SSPI). Existing word- phrase-based AAC solutions that employ BCIs utilize electroencephalography (EEG) are sometimes supplemented icons. Icon-based BCI systems use binary signaling methods, such as P300 detection, combine hierarchical layouts some form scanning. The rapid serial...
Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect and real task, making them non-ideal for complex scenarios. In this study we design a new, connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) context information effectively...
Even with state-of-the-art techniques there are individuals whose paralysis prevents them from communicating others. Brain⁻Computer-Interfaces (BCI) aim to utilize brain waves construct a voice for those needs remain unmet. In this paper we compare the efficacy of BCI input signal, code-VEP via Electroencephalography, against eye gaze tracking, among most popular modalities used. These results, on healthy without paralysis, suggest that while tracking works well some, it does not work or at...
Abstract High throughput DNA sequencing technologies have undergone tremendous development over the past decade. Although optical detection-based has constituted majority of data output, it requires a large capital investment and aggregation samples to achieve optimal cost per sample. We developed novel electronic platform capable accurately detecting single base incorporations. The GenapSys technology with its detection modality allows system be compact, accessible, affordable. demonstrate...
RSVP Keyboard™ is a non-invasive electroencephalography (EEG) based brain computer interface (BCI) for letter by typing. In this system sequence of symbols presented on screen in rapid serial visual presentation scheme to query user's intent. EEG evidence and language model are used conjunction joint inference the intended symbol. Usually repetition sequences necessary achieve high confidence symbol selection. This usually results degradation speed typing while compensating accuracy....
Auditory-evoked noninvasive electroencephalography (EEG) based brain-computer interfaces (BCIs) could be useful for improved hearing aids in the future. This manuscript investigates role of frequency and spatial features audio signal EEG activities an auditory BCI system with purpose detecting attended source a cocktail party setting. A cross correlation feature between speech envelope is shown to discriminate attention case two different speakers. Results indicate that, on average, speaker...