BCI Controlled Quadcopter Using SVM and Recursive LSE Implemented on ROS

Chhabra, Kshitij; Mathur, Pranay; Baths, Veeky

doi:10.1109/smc42975.2020.9282898

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…Another BCI that controls a virtual quadcopter was designed in [32]. Emotiv Epoc+ was used to acquire the raw EEG data from a single participant.…”

Section: ) Motor Imagerymentioning

confidence: 99%

“…The average number of electrodes is 31.7 which is the highest average of all categories. Most sensors used in these articles are sixty-four [36] [38] [39] followed by thirty-three [37], sixteen [35], fifteen [34], and fourteen [32]. The fewest electrodes used in this category are eight [27] [28].…”

Section: ) Statisticsmentioning

confidence: 99%

“…The fewest electrodes used in this category are eight [27] [28]. The average number of subjects is 13.22 ranging from sixty [27] to one [32]. [28] used twenty subjects which is the secondhighest number of participants followed by fourteen [35] and twelve [34].…”

Section: ) Statisticsmentioning

confidence: 99%

“…Five studies [37] [36] [27] [34] [39] employed four mental commands. [37] has a DoF of six, [32] has five and [35] has three.…”

Section: ) Statisticsmentioning

confidence: 99%

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Brain-Computer Interface Controlled Drones: A Systematic Review

Glavas,

Tzimourta,

Angelidis

et al. 2024

IEEE Access

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The goal of this systematic review is to examine the use of Brain-Computer Interfaces (BCIs) for controlling unmanned aerial vehicles (UAVs) in real-time. A comprehensive search across various online databases, including IEEE Explore, ScienceDirect, MDPI and PubMed, using the PRISMA research method, was conducted. The total of 42 experimental studies were identified, analyzed, and included in the final report. The review highlights several important research directions and areas that require further investigation in the field. Additionally, it identifies potential future possibilities and trends in BCI-controlled UAVs. Lastly, the review outlines the future challenges that researchers are likely to encounter, aiming to provide valuable insights and guidance for future studies in this area. To the authors' best knowledge, this systematic review represents the most extensive analysis in literature, both in terms of the number of studies included and the span of years considered.

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“…Another BCI that controls a virtual quadcopter was designed in [32]. Emotiv Epoc+ was used to acquire the raw EEG data from a single participant.…”

Section: ) Motor Imagerymentioning

confidence: 99%

Section: ) Statisticsmentioning

confidence: 99%

Section: ) Statisticsmentioning

confidence: 99%

“…Five studies [37] [36] [27] [34] [39] employed four mental commands. [37] has a DoF of six, [32] has five and [35] has three.…”

Section: ) Statisticsmentioning

confidence: 99%

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Brain-Computer Interface Controlled Drones: A Systematic Review

Glavas,

Tzimourta,

Angelidis

et al. 2024

IEEE Access

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“…Support vector machine (SVM) algorithms represent another alternative linear classification option which has been shown to perform well with non-parametric EEG features 60,61 . As such, this algorithm represents a popular method for BCI control [62][63][64] . SVM algorithms optimise the linear margin between features of each class to identify the margin which results in the least possible overlap between classes.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Optimising the classification of feature-based attention in frequency-tagged electroencephalography data

Renton¹,

Mattingley²,

Painter³

et al. 2021

UQ eSpace

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Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (featurebased attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). These signals are reliably present in single-trial electroencephalography (EEG) data, are largely resilient to common EEG artifacts, and allow separation of neural responses to numerous concurrently presented visual stimuli. To date, efforts to use single-trial SSVEPs to classify visual attention for BCI control have largely focused on spatial attention rather than featurebased attention. Here, we present a dataset that allows for the development and benchmarking of algorithms to classify feature-based attention using single-trial EEG data. The dataset includes EEG and behavioural responses from 30 healthy human participants who performed a feature-based motion discrimination task on frequency tagged visual stimuli.

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