Performance enhancement of wearable instrumentation for AR-based SSVEP BCI

Arpaïa, Pasquale; Benedetto, Egidio De; Paolis, Lucio Tommaso De; D’Errico, Giovanni; Donato, Nicola; Duraccio, Luigi

doi:10.1016/j.measurement.2022.111188

Cited by 18 publications

(7 citation statements)

References 39 publications

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“…For the sake of example, in [45] the output of the classification was associated to the frequency with the highest correlation coefficient extracted. Alternatively, in [1], [46], [47] the maximum value among the correlation coefficients ρ n was compared with given threshold values: the signal was marked as classified only if the chosen correlation coefficient exceeded the thresholds. The classification performance achieved with the use of CCA are typically better than PSDA [43].…”

Section: ) Non-ml Methodsmentioning

confidence: 99%

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

et al. 2023

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This work addresses the employment of Machine Learning (ML) and Domain Adaptation (DA) in the framework of Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs). Currently, all the state-of-the-art classification strategies do not consider the high non-stationarity typical of brain signals. This can lead to poor performance, expecially when short-time signals have to be considered to allow real-time human-environment interaction. In this regard, ML and DA techniques can represent a suitable strategy to enhance the performance of SSVEPs classification pipelines. In particular, the employment of a two-step DA technique is proposed: first, the standardization of the data per subject is performed by exploiting a part of unlabeled test data during the training stage; second, a similarity measure between subjects is considered in the selection of the validation sets. The proposal was applied to three classifiers to verify the statistical significance of the improvements over the standard approaches. These classifiers were validated and comparatively tested on a well-known public benchmark dataset. An appropriate validation method was used in order to simulate real-world usage. The experimental results show that the proposed approach significantly improves the classification accuracy of SSVEPs. In fact, up to 62.27 % accuracy was achieved also in the case of short-time signals (i.e., 1.0 s). This represents a further confirmation of the suitability of advanced ML to improve the performance of BCIs for daily-life applications.

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Section: ) Non-ml Methodsmentioning

confidence: 99%

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

et al. 2023

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“…Immersive technologies combine the physical world with VR to offer unique experiences. Hence, the underpinning of such a concept usually introduces hardware support for AR and VR environments to get a clear picture and understanding of such a phenomenon, such as the Microsoft Hololens and Oculus Rift initiatives (Arpaia et al, 2022) Various marketing research studies incorporating technology have addressed fragmented features of immersive technology (Fan et al, 2022;Johnson et al, 2022;Suh and Prophet, 2018). Yet, the exploration and exploitation of immersive technology usage for strategic marketing purposes remains unclear.…”

Section: Implications Of Immersive Technologymentioning

confidence: 99%

“…This implies that the strongest demand for using immersive technology initially comes from industries with innovative businesses that require adaptability, flexibility and a vision of consumer behavior. However, with the high cost investment in the devices related to immersive technology such as Microsoft HoloLens is relatively expensive, including insufficient broadband connections, affordability or digital literacy gaps (Arpaia et al , 2022). All issues could be a stumbling block to adopt it by small, medium, and enterprises (SMEs) at initial stage, although it provides multifunctionality services.…”

Section: Literature Reviewmentioning

confidence: 99%

Turning digital technology to immersive marketing strategy: a strategic perspective on flexibility, agility and adaptability for businesses

Yawised,

Apasrawirote,

Chatrangsan

et al. 2022

JEEE

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Purpose The purpose of this study is to conduct a systematic literature review of the adoption of immersive marketing technology (IMT) in terms of strategic planning of its adoption, resource requirements and its implications and challenges. Design/methodology/approach This study categorizes and contextualizes qualitative approaches to evaluate the literature, with Scopus databases serving as the primary source of 90 selected articles in the areas of information technology, business and marketing strands. Theme analysis was carried out using thematic techniques and grounded approach principles to facilitate thematic coding and generate theme analysis. Findings The analysis was supported by the three concepts of business flexibility, agility and adaptability, which were drawn as a strategy for IMT adoption. The findings presented three main themes: proactive flexibility, responsive agility and reactive adaptability that enable business owner–managers to craft a strategy for IMT adoption. Originality/value The novel contribution of this study is the inclusion of key implications related to IMT as a starting point of the next level of innovative marketing for all academics, practitioners and business owner–managers.

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“…E LECTROENCEPHALOGRAPHY is a well-established neuroimaging technique largely used to analyze brain activity, mostly in clinics and laboratories [1], [2]. Indeed, new electroencephalogram (EEG)-based applications for more practical use are being investigated, thanks to their noninvasiveness, ease of use, and potential wearability and portability [3], [4], [5], [6], [7]. However, the use of the EEG outside of clinical and research settings is still limited because the cerebral signal is heavily influenced by noise and interference, leading to a variety of artifacts [8] that compromise the correct extraction of the features of interest [9].…”

Section: Introductionmentioning

confidence: 99%

A Method for Optimizing the Artifact Subspace Reconstruction Performance in Low-Density EEG

et al. 2022

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Electroencephalogram (EEG) plays a significant role in the analysis of cerebral activity, although the recorded electrical brain signals are always contaminated with artifacts. This represents the major issue limiting the use of the EEG in daily life applications, as the artifact removal process still remains a challenging task. Among the available methodologies, artifact subspace reconstruction (ASR) is a promising tool that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameters have been validated only for high-density EEG acquisitions. In this regard, this study proposes an enhanced procedure for the optimal individuation of ASR parameters, in order to successfully remove artifacts in low-density EEG acquisitions (down to four channels). The proposed method starts from the analysis of real EEG data, to generate a large semisimulated dataset with similar characteristics. Through a fine-tuning procedure on this semisimulated data, the proposed method identifies the optimal parameters to be used for artifact removal on real data. The results show that the algorithm achieves an efficient removal of artifacts preserving brain signal information, also in low-density EEG signals, thus favoring the adoption of the EEG also for more portable and/or daily-life applications.

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Performance enhancement of wearable instrumentation for AR-based SSVEP BCI

Cited by 18 publications

References 39 publications

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

Turning digital technology to immersive marketing strategy: a strategic perspective on flexibility, agility and adaptability for businesses

A Method for Optimizing the Artifact Subspace Reconstruction Performance in Low-Density EEG

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