An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Rashid, Usman; Niazi, Imran Khan; Signal, Nada; Taylor, Denise

doi:10.3390/s18113721

Cited by 56 publications

(53 citation statements)

References 52 publications

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“…The latencies of BP1, BP2 and PN, and the amplitudes of BP1, BP2, PN obtained from the sum of the two ERPs at the latencies of BP1, BP2, time of PN provided the ground-truth data. A white noise was added to the sum of ERPs to produce simulated MRCPs under 3 signal to noise ratios (SNR) (6 dB, 3 dB, 0 dB) [31]. The signal to noise ratio was set by changing the root mean square value of the white noise with respect to the peak amplitude of the second ERP which corresponds to amplitude of PN.…”

Section: B Proposed Methods For Simulation Of Mrcpsmentioning

confidence: 99%

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Automated Labeling of Movement- Related Cortical Potentials Using Segmented Regression

Rashid

Niazi

Jochumsen

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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The movement-related cortical potential (MRCP) is a brain signal related to planning and execution of motor tasks. From an MRCP, three notable features can be identified: the early Bereitschaftspotential (BP1), the late Bereitschaftspotential (BP2), and the negative peak (PN). These features have been used in past studies to quantify neurophysiological changes in response to motor training. Currently, either manual labeling or a priori specification of time points is used to extract these features. The limitation of these methods is the inability to fully model the features. This paper proposes the segmented regression along with a local peak method for automated labeling of the features. The proposed method derives the onsets, amplitudes at onsets, and slopes of BP1 and BP2 along with time and amplitude of the PN in a typical average MRCP. To choose the most suitable regression technique a bounded segmented regression method, a change point method and multivariate adaptive regression splines were evaluated using the root-mean-square error on a dataset of 6000 simulated MRCPs. The best-performing regression technique combined with the local peak method was then applied to a smaller set of 123 simulated MRCPs. Error in onsets of BP1 and BP2 and time of PN were compared with the errors in manual labeling by an expert. The performance of the proposed method was also evaluated on an experimental dataset of MRCPs derived from electroencephalography (EEG) recorded across two sessions from 22 healthy participants during a lower limb task. The Bland-Altman plots were used to evaluate the absolute reliability of the proposed method. On experimental data, the proposed method was also compared with manual labeling by an expert.

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Section: B Proposed Methods For Simulation Of Mrcpsmentioning

confidence: 99%

“…The experimental MRCPs used in this research were recorded from 22 healthy participants (Average age: 36 ± 6 years, 10 female) who were recruited through professional networks and local advertising [31]. Participants were excluded if they had a history of any neurological disorders or epilepsy.…”

Section: Experimental Mrcpsmentioning

confidence: 99%

Automated Labeling of Movement- Related Cortical Potentials Using Segmented Regression

Rashid

Niazi

Jochumsen

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Hence, the ability to record MRCPs and ERD is crucial for any neurorehabilitation BCI, and this ability has been less explored in commercial headsets. A single study compared the signal quality of MRCP when recorded with two different amplifiers but with the same headset [42], and a couple of studies investigated if ERD patterns could be identified using dry and wet electrodes [27,28]. Thus, there is a need for an evaluation of different headset types that potentially could be used for BCI training in neurorehabilitation clinics.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent study, Oliveira et al [39] proposed a number of metrics to investigate the signal quality when comparing different headsets. These metrics include data or epoch rejection rate [16,39,40,43] and signal-to-noise ratio (SNR) or noise level [16,32,39,42,44]. This is also important for BCI applications, since the performances of pattern recognition techniques are affected by various types of noise that cannot be suppressed.…”

Section: Introductionmentioning

confidence: 99%

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Jochumsen

Knoche

Kjær

et al. 2020

Sensors

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Brain–computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets’ ability to record and classify movement intentions (movement-related cortical potentials—MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%−77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.

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“…Electroencephalography (EEG) is a means of obtaining data through sensors (Rashid et al, 2018 ; Uktveris and Jusas, 2018 ). The brain-computer interface (BCI), also known as a direct neural interface, is an interdisciplinary cutting-edge technology that represents a direct link between human or animal brains (or brain cell cultures) and external devices (Wolpaw et al, 2000 , 2002 ; Cecotti, 2011 ; Chaudhary et al, 2016 ; Ramadan and Vasilakos, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Sparse Logistic Regression With L1/2 Penalty for Emotion Recognition in Electroencephalography Classification

Chen

Miao

Deng

et al. 2020

Front. Neuroinform.

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Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (L 1) and Ridge Regression (L 2). In recent years, researchers have proposed many other regularization terms. In theory, L q-type regularization has a lower q value, which means that it can be used to find solutions with better sparsity. L 1/2 regularization is of L q type (0 < q < 1) and has been shown to have many attractive properties. In this work, we studied the L 1/2 penalty in sparse logistic regression for three-classification EEG emotion recognition, and used a coordinate descent algorithm and a univariate semi-threshold operator to implement L 1/2 penalty logistic regression. The experimental results on simulation and real data demonstrate that our proposed method is better than other existing regularization methods. Sparse logistic regression with L 1/2 penalty achieves higher classification accuracy than the conventional L 1 , Ridge Regression, and Elastic Net regularization methods, using fewer but more informative EEG signals. This is very important for high-dimensional small-sample EEG data and can help researchers to reduce computational complexity and improve computational accuracy. Therefore, we propose that sparse logistic regression with the L 1/2 penalty is an effective technique for emotion recognition in practical classification problems.

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An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Cited by 56 publications

References 52 publications

Automated Labeling of Movement- Related Cortical Potentials Using Segmented Regression

Automated Labeling of Movement- Related Cortical Potentials Using Segmented Regression

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

Sparse Logistic Regression With L1/2 Penalty for Emotion Recognition in Electroencephalography Classification

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