Performance of EEG Motor-Imagery based spatial filtering methods: A BCI study on Stroke patients

Miladinović, Aleksandar; Ajčević, Miloš; Jarmolowska, Joanna; Marušič, Uroš; Silveri, Giulia; Battaglini, Piero Paolo; Accardo, Agostino

doi:10.1016/j.procs.2020.09.270

Cited by 15 publications

(8 citation statements)

References 44 publications

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“…These findings may represent a breakthrough in the research, as those brain wave changes may represent the hallmark of EEG abnormalities in PD that can be important to better understand the disease, monitor its progress and evaluate treatment efficacy. Furthermore, the design of Brain-Computer Interface motor rehabilitation [20], feasible in neurologic disorders [21,22], and in control of assistive robots [23] could be improved by understanding EEG changes in PDs. Indeed, EEG changes over time can introduce nonstationarities and diminish BCI accuracy [24].…”

Section: Introductionmentioning

confidence: 99%

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

Miladinović

Ajčević

Busan

et al. 2021

Procedia Computer Science

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Section: Introductionmentioning

confidence: 99%

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

Miladinović

Ajčević

Busan

et al. 2021

Procedia Computer Science

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“…Several studies have applied BCI systems to patients with stroke (Hortal et al, 2015 ; Bhagat et al, 2016 ; Mrachacz-Kersting et al, 2017 ; Miladinović et al, 2020 ; Niazi et al, 2022 ). Table 5 compares the proposed method with existing studies.…”

Section: Discussionmentioning

confidence: 99%

“…For a fair comparison, we checked the false positives and experimental paradigms to calculate the FP rate (equation 5). The study by Hortal et al (2015), Bhagat et al (2016), Mrachacz-Kersting et al (2017), and Miladinović et al (2020 used the same calculation method as our study to report the FP rate. The study by Niazi et al (2022) reported false positive per minute, true positive rate, and percentage of false positives over true positive.…”

Section: Discussionmentioning

confidence: 99%

An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study

et al. 2022

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Many studies have used motor imagery-based brain–computer interface (MI-BCI) systems for stroke rehabilitation to induce brain plasticity. However, they mainly focused on detecting motor imagery but did not consider the effect of false positive (FP) detection. The FP could be a threat to patients with stroke as it can induce wrong-directed brain plasticity that would result in adverse effects. In this study, we proposed a rehabilitative MI-BCI system that focuses on rejecting the FP. To this end, we first identified numerous electroencephalogram (EEG) signals as the causes of the FP, and based on the characteristics of the signals, we designed a novel two-phase classifier using a small number of EEG channels, including the source of the FP. Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71.76% selectivity and 13.70% FP rate by using only four EEG channels in the patient group with stroke. Moreover, our system can compensate for day-to-day variations for prolonged session intervals by recalibration. The results suggest that our proposed system, a practical approach for the clinical setting, could improve the therapeutic effect of MI-BCI by reducing the adverse effect of the FP.

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“…BCI methods for the lower extremities were previously used in the context of neurorehabilitation in stroke patients (Miladinović et al, 2020b ) and patients with Parkinson's disease (Miladinović et al, 2020a ). However, such studies are lacking in the context of lower limb amputation.…”

Section: Introductionmentioning

confidence: 99%

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics

Dillen

Lathouwers²,

Miladinović³

et al. 2022

Front. Hum. Neurosci.

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Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.

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Performance of EEG Motor-Imagery based spatial filtering methods: A BCI study on Stroke patients

Cited by 15 publications

References 44 publications

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands

An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics

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