Wearable Sensing’s wireless DSI-Flex is the leading dry electrode EEG system in terms of signal quality and comfort. The DSI-Flex takes on average less than 5 minutes to set up, making it the ideal solution for scientists in need of a simple, easy to use, EEG system. Our patented sensor technology not only delivers uncompromised signal quality but also enables our system to be virtually immune against motion and electrical artifacts.
The DSI-Flex has dry sensors on flexible cables, enabling scientists to place the electrodes in varying configurations on the head. These flexible sensors are designed to be screwed into custom caps, so that scientists can order 1 DSI-Flex, and multiple caps, allowing for rapid application of multiple electrode configurations. Every sensor on the DSI-Flex can be customized as either ExG, GSR, TEMP, and REP. It also has a 4-bit trigger input to synchronize with other devices such as Eye-Tracking, Motion (IMU), and more.
Used around the world by leaders in Research, & Brain-Computer Interfaces
With over 90% correlation to research-grade wet EEG systems, the dry sensor interface (DSI) offers unparalleled quality and performance
Multiple adjustment points and a foam pad lined interior enable the system to be worn for up to 8 hours on any head shape or size
All DSI systems include free, unlimited licenses of DSI-Streamer, our data acquisition software which can record raw data, in .csv and .edf file formats
Faraday cage's, spring-loaded electrodes, and our patented common-mode follower technology, provides near immunity against electrical and motion artifacts
Using 70% isopropyl alcohol and a cleaning brush, the DSI-24 only takes a minute to clean, 3 minutes to dry, and can be up and running on the next subject in minutes
All DSI systems include our free C based .dll API, which enables users to pull the raw data directly from the headset, for custom software on Windows, Mac OS, Linux, and ARM
The DSI-Flex was designed for ultra-rapid setup, taking on average less than 5 minutes to don, and works on any type of hair, including long hair, thick hair, afros, and more
DSI headsets have active sensors, amplifiers, digitizers, batteries, onboard storage, and wireless transmission, making them complete, mobile, wearable EEG systems
DSI systems exclusively work with QStates, a machine learning algorithm for cognitive classification on states such as mental workload, engagement, and fatigue
Our Wireless Trigger Hub simplifies the synchronization of DSI headsets with other devices. It features:
An additional benefit of the Trigger Hub design is that it allows synchronization across multiple data sources that are distributed across multiple systems, each of which running at its own clock rate. One such case commonly experienced in EEG experiments involves the synchronization of EEG and eye-tracking measurements, where the inevitable clock drift that arises between two systems during extended measurements creates difficulty in aligning data to events across the two systems.
The DSI-Flex can be customized so that an EEG sensor is replaced with a DSI auxiliary sensor. There are up to 7 locations on the DSI-Flex, enabling any configuration of the following sensors: EEG, ECG, EMG, EOG, GSR, RESP, & TEMP. The sensor data is collected and recorded in our data acquisition software, DSI-Streamer, where you can view the EEG and Aux sensors in real-time.
EEG Channels
Up to 7 Custom Sensor Locations
Reference / Ground
Common Mode Follower / Custom
Head Size Range
Custom Caps
Sampling Rate
300 Hz (600Hz upgrade available)
Bandwidth
0.003 – 150 Hz
A/D resolution
0.317 μV referred to input
Input Impedance (1Hz)
47 GΩ
CMRR
> 120 dB
Amplifier / Digitizer
16 bits / 7 channels
Wireless
Bluetooth
Wireless Range
10 m
Run-time
> 12 hours
Onboard Storage
~ 68 Hours (available option)
Data Acquisition
Real time, evoked potentials
Signal Quality Monitoring
Continuous impedance, Baseline offset, Noise (1-50 Hz)
Data Type
Raw and Filtered Data available
File Type
.CSV and .EDF
Data Output Streaming
TCP/IP socket, API (C Based), LSL
Cognitive State Classification
Brain Computer Interface
SSVEP BCI Algorithms; BCI2000; OpenViBE; PsychoPy; BCILab
Data Integration / Analysis
CAPTIV; Lab Streaming Layer; NeuroPype; BrainStorm; NeuroVIS
Neurofeedback
Applied Neuroscience NeuroGuide; Brainmaster Brain Avatar; EEGer
Neuromarketing
CAPTIV Neurolab
Presentation
Presentation; E-Prime
Kyrou, Maria; Laskaris, Nikos A; Petrantonakis, Panagiotis C; Kalaganis, Fotis P; Georgiadis, Kostas; Nikolopoulos, Spiros; Kompatsiaris, Ioannis
Decoding Visual Art Preferences from EEG Signals Using Wavelet Scattering Transform Conference
2025 25th International Conference on Digital Signal Processing 2025.
@conference{kyroudecoding,
title = {Decoding Visual Art Preferences from EEG Signals Using Wavelet Scattering Transform},
author = {Maria Kyrou and Nikos A Laskaris and Panagiotis C Petrantonakis and Fotis P Kalaganis and Kostas Georgiadis and Spiros Nikolopoulos and Ioannis Kompatsiaris},
url = {https://2025.ic-dsp.org/wp-content/uploads/2025/05/Decoding-Visual-Art-Preferences-from-EEG-signals-Using-Wavelet-Scattering-Transform.pdf},
year = {2025},
date = {2025-06-25},
organization = {2025 25th International Conference on Digital Signal Processing},
abstract = {Understanding art preferences through neural signals can enhance artistic experiences and provide valuable insights into aesthetic perception. In this study, we propose a novel EEG-based framework for visual art preferences classification, leveraging Wavelet Scattering Transform (WST) for feature extraction and Support Vector Machines (SVM) for classification. Unlike deep learning approaches that require large-scale datasets and extensive training, a wavelet scattering network provides low-variance, translation-invariant features without the need for learnable parameters, making it well-suited for regular size EEG datasets. Experimental results demonstrate that the proposed method effectively differentiates between ”like” and ”dislike” ratings based on EEG responses to visual art stimuli. The findings highlight the potential of wavelet scattering-based feature extraction in decoding aesthetic preferences.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Kalaganis, Fotis P; Georgiadis, Kostas; Nousias, Georgios; Oikonomou, Vangelis P; Laskaris, Nikos A; Nikolopoulos, Spiros; Kompatsiaris, Ioannis
Enhancing EEG-Based Neuromarketing with Attention mechanism and Riemannian Features Conference
2025 25th International Conference on Digital Signal Processing 2025.
@conference{kalaganisenhancing,
title = {Enhancing EEG-Based Neuromarketing with Attention mechanism and Riemannian Features},
author = {Fotis P Kalaganis and Kostas Georgiadis and Georgios Nousias and Vangelis P Oikonomou and Nikos A Laskaris and Spiros Nikolopoulos and Ioannis Kompatsiaris},
url = {https://2025.ic-dsp.org/wp-content/uploads/2025/05/IEEE_DSP_Attention_and_SCMs_for_Neuromarketing_final.pdf},
year = {2025},
date = {2025-06-25},
organization = {2025 25th International Conference on Digital Signal Processing},
abstract = {This paper presents a novel EEG-based decoding framework that integrates Riemannian Geometry features with Deep Learning to enhance neuromarketing classification tasks. The proposed approach leverages Spatial Covariance Matrices (SCMs) computed across multiple frequency bands and employs a self-attention mechanism to improve feature selection and classification performance. To address the challenges of class imbalance and inter-subject variability, Riemannian alignment and data augmentation techniques are incorporated, ensuring robust feature representations. The framework is evaluated on the NeuMa dataset, where participants engaged in a realistic shopping scenario. Experimental results demonstrate that the proposed method achieves a balanced accuracy of 77.80%, outperforming traditional classifiers, including Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Riemannian-based models, and SPDNet-based approaches. These findings highlight the effectiveness of combining functional covariation with deep learning architectures, paving the way for more advanced EEGbased consumer behavior analysis in neuromarketing applications.
},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Parra, Sebastian Rueda; Hardesty, Russell Lee; GEMOETS, DARREN Ethan; Hill, Jeremy; Gupta, Disha
Test-Retest Reliability of Kinematic and EEG Low-beta spectral features in a robot-based arm movement task Journal Article
In: Biomedical Physics & Engineering Express, 2025.
@article{rueda2025test,
title = {Test-Retest Reliability of Kinematic and EEG Low-beta spectral features in a robot-based arm movement task},
author = {Sebastian Rueda Parra and Russell Lee Hardesty and DARREN Ethan GEMOETS and Jeremy Hill and Disha Gupta},
doi = {https://doi.org/10.1088/2057-1976/ade317},
year = {2025},
date = {2025-06-10},
urldate = {2025-01-01},
journal = {Biomedical Physics & Engineering Express},
abstract = {Objective: Low-beta (Lβ, 13-20 Hz) power plays a key role in upper-limb motor control and afferent processing, making it a strong candidate for a neurophysiological biomarker. We investigate the test-retest reliability of Lβ power and kinematic features from a robotic task over extended intervals between sessions to assess its potential for tracking longitudinal changes in sensorimotor function.
Approach: We designed and optimized a testing protocol to evaluate Lβ power and kinematic features (maximal and mean speed, reaction time, and movement duration) in ten right-handed healthy individuals that performed a planar center-out task using a robotic device and EEG for data collection. The task was performed with both hands, and the experiment was repeated approximately 40 days later under similar conditions, to resemble real-life intervention periods. We first characterized the selected features within the task context for each session, then assessed intersession agreement, the test-retest reliability (Intraclass Correlation Coefficient, ICC), and established threshold values for meaningful changes in Lβ power using Bland-Altman plots and repeatability coefficients.
Main results: Lβ power showed the expected contralateral reduction during movement preparation and onset. Both Lβ power and kinematic features exhibited good to excellent test-retest reliability (ICC > 0.8), displaying no significant intersession differences. Kinematic results align with prior literature, reinforcing the robustness of these measures in tracking motor performance over time. Changes in Lβ power between sessions exceeding 11.4% for right-arm and 16.5% for left-arm movements reflect meaningful intersession differences.
Significance: This study provides evidence that Lβ power remains stable over extended intersession intervals comparable to rehabilitation timelines. The strong reliability of both Lβ power and kinematic features supports their use in monitoring upper-extremity sensorimotor function longitudinally, with Lβ power emerging as a promising biomarker for tracking therapeutic outcomes, postulating it as a reliable feature for long-term applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wang, Hongli; Shi, Xiaoning; Wang, Chenyang; Qi, Yongsheng; Gao, Michel; Zhao, Yingying
Cross-frequency coupling between low frequency and gamma oscillations altered in cognitive biotype of depression Journal Article
In: Frontiers in Psychiatry, vol. 16, pp. 1596191, 2025.
@article{wang2025cross,
title = {Cross-frequency coupling between low frequency and gamma oscillations altered in cognitive biotype of depression},
author = {Hongli Wang and Xiaoning Shi and Chenyang Wang and Yongsheng Qi and Michel Gao and Yingying Zhao},
doi = {https://doi.org/10.3389/fpsyt.2025.1596191},
year = {2025},
date = {2025-06-09},
urldate = {2025-01-01},
journal = {Frontiers in Psychiatry},
volume = {16},
pages = {1596191},
publisher = {Frontiers Media SA},
abstract = {Aim: The cognitive biotype of depression has been conceptualized as a distinct subtype characterized by unique distinct neural correlates and specific clinical features. Abnormal neural oscillations related to cognitive dysfunction have been extensively studied, with particular attention given to gamma oscillations due to their crucial role in neurocircuit operations, emotional processing, and cognitive functions. Nevertheless, cross-frequency coupling between low frequency and gamma oscillations in the cognitive biotype of depression have yet to be fully elucidated.
Method: The study identified the cognitive biotype in depression by MATRICS Consensus Cognitive Battery (MCCB). We enrolled 141 depressed patients in remission, including 56 identified as cognitive biotype and 85 as the non-cognitive impairment subgroup. Cross-frequency coupling between low frequency and gamma oscillations were analyzed using specific computational methods based on the data collected by Electroencephalogram (EEG). Furthermore, we did correlation analysis to explore the relationship between cross-frequency coupling of neural oscillations with cognitive function in depression.
Results: We found that phase-amplitude coupling (PAC) values decreased in cognitive biotype. Specifically, cross-frequency coupling between theta (Pz: t =-3.512, FDR-corrected p = 0.011), alpha (P3: t =-3.377, FDR-corrected p = 0.009; Pz: t =-3.451, FDR-corrected p = 0.009), beta (P3: t =-3.129, FDR-corrected p = 0.020; Pz: t =-3.333, FDR-corrected p = 0.020) with low gamma decreased at eyes-closed state in cognitive biotype. However, cross-frequency coupling between delta with gamma increased in cognitive biotype (P4: t = 3.314, FDR-corrected p = 0.022) While cross-frequency coupling exhibited no significant differences at eyes-opened state in two subgroups (FDR-corrected p > 0.05). Furthermore, significant correlations between cognitive function and cross-frequency coupling at eyes-closed state were observed.
Conclusion: These results indicated that the cross-frequency coupling between low frequency and gamma occurred in the parietal lobe in cognitive biotype of depression. These results advance the understanding of neurophysiological mechanisms underlying cognitive deficits and highlight potential biomarkers for precision depression.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kim, Heegyu; Jun, Sung Chan; Nam, Chang S
Neural Dynamics of Group Interaction in the Iterate Multi-player Prisoner’s Dilemma Game: Multilayer Network Approach Proceedings Article
In: International Conference on Human-Computer Interaction, pp. 18–31, Springer 2025.
@inproceedings{kim2025neural,
title = {Neural Dynamics of Group Interaction in the Iterate Multi-player Prisoner’s Dilemma Game: Multilayer Network Approach},
author = {Heegyu Kim and Sung Chan Jun and Chang S Nam},
url = {https://link.springer.com/chapter/10.1007/978-3-031-93724-8_2},
year = {2025},
date = {2025-05-25},
urldate = {2025-01-01},
booktitle = {International Conference on Human-Computer Interaction},
pages = {18–31},
organization = {Springer},
abstract = {Understanding social interaction from various human behaviors is a complex task. Hyperscanning research tackles this challenge by delving into behavioral mechanisms through a neuroscience lens. While traditional studies focus on inter-brain synchrony in paired functional brain networks, they often lack methods for measuring interactions at the group level. In this study, we propose a multilayer network approach to estimate group brain synchrony and gain deeper insights into the brain’s intricate organization. By utilizing the Prisoner’s Dilemma Game, our goal is to find group interaction processes through distinct behaviors such as cooperation and defection. Thus, the inter-brain synchrony along with differences in network connectivity and structural properties within the functional group network were statistically analyzed between cooperation and defection.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nyffenegger, Daniela; Baur, Heiner; Henle, Philipp; Busch, Aglaja
In: The Knee, vol. 55, pp. 168–178, 2025.
@article{nyffenegger2025cortical,
title = {Cortical activity during the first 4 months after anterior cruciate ligament reconstruction while performing an active knee joint position sense test: A pilot study},
author = {Daniela Nyffenegger and Heiner Baur and Philipp Henle and Aglaja Busch},
doi = {https://doi.org/10.1016/j.knee.2025.04.017},
year = {2025},
date = {2025-05-07},
urldate = {2025-01-01},
journal = {The Knee},
volume = {55},
pages = {168–178},
publisher = {Elsevier},
abstract = {Background
Anterior cruciate ligament (ACL) rupture is thought to alter the way in which the brain receives and processes information, affecting body movements. Although alterations in brain activity after ACL rupture have been described, these are limited to time points more than 6 months after rupture. Therefore, this pilot study aims to investigate cortical activity during an active knee joint position sense (JPS) test within the first 4 months after ACL reconstruction.
Methods
Twelve participants with ACL reconstruction (nine males; age 25.3 ± 6.4 years; height 173.6 ± 8.0 cm; mass 71.1 ± 9.1 kg) and 12 matched healthy controls (nine males; age 28.8 ± 9.7 years; height 174.5 ± 9.7 cm; mass 72.7 ± 12.7 kg) performed an active knee JPS test in an open kinetic chain with a starting angle of 90° knee flexion and a target angle of 50°. Absolute angular error was measured with an electrogoniometer. Cortical activity was simultaneously recorded with dry electroencephalography. Participants with ACL reconstruction were measured at 5–8 weeks postoperative (M1) and 12–16 weeks postoperative (M2), the control group once. Power spectra for the frequencies, theta (4.75–6.75 Hz), alpha-1 (7.0–9.5 Hz) and alpha-2 (9.75–12.5 Hz) for frontal, central and parietal regions of interest were calculated.
Results
Participants with ACL reconstruction exhibited significantly higher central theta power during JPS testing with their uninvolved leg at M1 compared with M2 (adjusted P = 0.01; rank epsilon squared = 0.39). No other comparisons yielded statistically significant differences.
Conclusions
The results cautiously support current evidence on cortical alterations following ACL reconstruction. A larger sample size and more measurement time points may provide further insight into possible alterations in the early postoperative period.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lim, Hyunmi; Ku, Jeonghun
Effects of Augmented Reality (AR) Mirror Therapy Combined with Peripheral Electrical Journal Article
In: Emerging Therapies in Neurorehabilitation III: Proceedings of the 10/11/12th Summer School on Neurorehabilitation (SSNR), held in 2022, 2023, 2024, in Baiona, Spain, vol. 34, pp. 100, 2025.
@article{lim2025effects,
title = {Effects of Augmented Reality (AR) Mirror Therapy Combined with Peripheral Electrical},
author = {Hyunmi Lim and Jeonghun Ku},
url = {https://books.google.com/books?hl=en&lr=&id=fx1TEQAAQBAJ&oi=fnd&pg=PA100&dq=Effects+of+Augmented+Reality+(AR)+Mirror+Therapy+Combined+with+Peripheral+Electrical&ots=jIWVkp4Jhf&sig=9Y7R_WCksKg36QGoCge2JwgHwTU#v=onepage&q=Effects%20of%20Augmented%20Reality%20(AR)%20Mirror%20Therapy%20Combined%20with%20Peripheral%20Electrical&f=false},
year = {2025},
date = {2025-04-05},
urldate = {2025-01-01},
journal = {Emerging Therapies in Neurorehabilitation III: Proceedings of the 10/11/12th Summer School on Neurorehabilitation (SSNR), held in 2022, 2023, 2024, in Baiona, Spain},
volume = {34},
pages = {100},
publisher = {Springer Nature},
abstract = {Abstract. In this study, we investigated the effects of an augmented reality (AR)- based mirror therapy program that combines peripheral electrical stimulation (PES) with mirror visual feedback (MVF) on brain activity. The program was tested on three healthy adults in their 20s. The results showed that the combined feedback of MVF and PES was effective in enhancing motor learning-related brain activity in the mirror-reflected hand. Further research will investigate the effects of different AR feedback factors on brain activity, which will aid in developing optimal rehabilitation protocols. These findings could be applied to rehabilitation therapy for stroke patients, potentially improving the effectiveness of their treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gupta, Disha; Brangaccio, Jodi Ann; Mojtabavi, Helia; Carp, Jonathan S; Wolpaw, Jonathan R; Hill, N Jeremy
Frequency dependence of cortical somatosensory evoked response to peripheral nerve stimulation with controlled afferent excitation Journal Article
In: Journal of Neural Engineering, 2025.
@article{gupta2025frequency,
title = {Frequency dependence of cortical somatosensory evoked response to peripheral nerve stimulation with controlled afferent excitation},
author = {Disha Gupta and Jodi Ann Brangaccio and Helia Mojtabavi and Jonathan S Carp and Jonathan R Wolpaw and N Jeremy Hill},
doi = {10.1088/1741-2552/adc204},
year = {2025},
date = {2025-03-18},
urldate = {2025-01-01},
journal = {Journal of Neural Engineering},
abstract = {Objective: H-reflex Targeted Neuroplasticity (HrTNP) protocols comprise a promising rehabilitation approach to improve motor function after brain or spinal injury. In this operant conditioning protocol, concurrent measurement of cortical responses, such as somatosensory evoked potentials (SEPs), would be useful for examining supraspinal involvement and neuroplasticity mechanisms. To date, this potential has not been exploited. However, the stimulation parameters used in the HrTNP protocol deviate from the classically recommended settings for SEP measurements. Most notably, it demands a much longer pulse width, higher stimulation intensity, and lower frequency than traditional SEP settings. In this paper, we report SEP measurements performed within the HrTNP stimulation parameter constraints, specifically characterizing the effect of stimulation frequency. Approach: SEPs were acquired for tibial nerve stimulation at three stimulation frequencies (0.2, 1, and 2 Hz) in 13 subjects while maintaining the afferent volley by controlling the direct soleus muscle response via the Evoked Potential Operant Conditioning System. The amplitude and latency of the short-latency P40 and mid-latency N70 SEP components were measured at the central scalp region using non-invasive electroencephalography. Results: As frequency rose from 0.2Hz, P40 amplitude and latency did not change. In contrast, N70 amplitude decreased significantly (39% decrease at 1 Hz, and 57% decrease at 2Hz), presumably due to gating effects. N70 latency was not affected. Across all three frequencies, N70 amplitude increased significantly with stimulation intensity and correlated with M-wave amplitude. Significance: We assess SEPs within an HrTNP protocol, focusing on P40 and N70, elicited with controlled afferent excitation at three stimulation frequencies. HrTNP conditioning protocols show promise for enhancing motor function after brain and spinal injuries. While SEPs offer valuable insights into supraspinal involvement, the stimulation parameters in HrTNP often differ from standard SEP measurement protocols. We address these deviations and provide recommendations for effectively integrating SEP assessments into HrTNP studies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rustamov, Nabi; Demarest, Phillip; Han, Zhuangyu; Mohamud, Safia; Haroutounian, Simon; Leuthardt, Eric C
Cortical Spectral Dynamics of Vibrotactile Frequency Processing Journal Article
In: Scientific Reports, 2025.
@article{rustamov2025cortical,
title = {Cortical Spectral Dynamics of Vibrotactile Frequency Processing},
author = {Nabi Rustamov and Phillip Demarest and Zhuangyu Han and Safia Mohamud and Simon Haroutounian and Eric C Leuthardt},
doi = {https://doi.org/10.21203/rs.3.rs-6043161/v1},
year = {2025},
date = {2025-02-21},
urldate = {2025-01-01},
journal = {Scientific Reports},
abstract = {While scientific research has extensively explored how the brain integrates touch and pain signals, the cerebral processing of specific vibrotactile frequencies remains poorly understood. This gap is particularly significant given clinical evidence that vibrotactile stimulation can reduce pain in both chronic pain patients and experimental settings. Our study investigated the cortical electrophysiological correlates of peripheral vibrotactile stimulation across different frequencies in healthy volunteers, with a focus on frequency-dependent patterns of neuronal activation. While electroencephalogram (EEG) was recorded, healthy participants received vibrotactile stimulation to the left index fingertip at frequencies corresponding to established neural rhythms: delta (2 Hz), theta (6 Hz), alpha (12 Hz), beta (20 Hz), and gamma (40 Hz). We compared the EEG bandwidth activity between vibrotactile stimulation conditions relative to resting baseline. Our findings demonstrated that vibrotactile stimulation produces distinct frequency-dependent patterns of cortical activation. A key finding was that 6 Hz stimulation selectively enhanced theta power in the left prefrontal cortex - an electrophysiological signature previously linked to successful pain relief. These findings advance the understanding of the "spectrotopic" nature of vibrotactile frequency processing in the cortex and provide a mechanistic foundation for developing novel vibration-based therapies in the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Peters, Betts; Celik, Basak; Gaines, Dylan; Galvin-McLaughlin, Deirdre; Imbiriba, Tales; Kinsella, Michelle; Klee, Daniel; Lawhead, Matthew; Memmott, Tab; Smedemark-Margulies, Niklas; others,
In: Journal of Neural Engineering, 2025.
@article{peters2025rsvp,
title = {RSVP Keyboard with Inquiry Preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input},
author = {Betts Peters and Basak Celik and Dylan Gaines and Deirdre Galvin-McLaughlin and Tales Imbiriba and Michelle Kinsella and Daniel Klee and Matthew Lawhead and Tab Memmott and Niklas Smedemark-Margulies and others},
doi = {10.1088/1741-2552/ada8e0},
year = {2025},
date = {2025-01-10},
urldate = {2025-01-01},
journal = {Journal of Neural Engineering},
abstract = {Objective. The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce Inquiry Preview, a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes. Approach. Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of Inquiry Preview and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of Inquiry Preview and two modes of switch input on typing accuracy and speed during a copy-spelling task. Main results. For participants without disabilities, Inquiry Preview and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience with Inquiry Preview and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with Inquiry Preview and switch input and gave favorable user experience ratings to those conditions, but preferred standard RSVP Keyboard. Significance. Inquiry Preview is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and user experience and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. ClinicalTrials.gov Identifier: NCT04468919.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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