Brain-computer interface speller system design from electroencephalogram signals with channel selection algorithms

Khairullah, Enas F.; Arıcan, Murat; Polat, Kemal

doi:10.1016/j.mehy.2020.109690

Cited by 4 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This issue has been addressed in several recent studies. For instance, using a genetic algorithm for controlling a speller system with 85 letters, 8 out of 64 channels were selected, resulting in an average accuracy of 96% [72]. The present study demonstrates that a high performance of movement detection can be achieved using a single channel and participant-specific parameter tuning (>80%).…”

Section: Discussionmentioning

confidence: 71%

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

Aliakbaryhosseinabadi

Došen

Savić

et al. 2021

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Brain–computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis. Approach. Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis. Main results. The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%). Significance. The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.

show abstract

Section: Discussionmentioning

confidence: 71%

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

Aliakbaryhosseinabadi

Došen

Savić

et al. 2021

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…To verify the performance of RSBSBL, we compared the proposed method with the state-of-the-art developments in recent years on DS2, as shown in based on evolutionary computational algorithms (Kee et al, 2015;Khairullah et al, 2020;Tang et al, 2020;Martinez-Cagigal et al, 2022). The channel selection methods and classifiers used in each study are shown in the table.…”

Section: Character Recognition Performancementioning

confidence: 99%

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

Zhao

Jin

et al. 2022

Front. Hum. Neurosci.

View full text Add to dashboard Cite

The P300-based brain–computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.

show abstract

“…Epilepsy is characterized by a body movement that results in excessive discharge of groups in brain cells and transition disorders, and sudden changes in mental functions. Epileptic EEG signals from the scalp are characterized by high amplitude and synchronized periodic waveforms ( Patnaik & Manyam, 2008 ; Acir et al, 2005 ; Ozdemir & Polat, 2020 ; Daldal, Nour & Polat, 2020 ; Daldal, Polat & Guo, 2019 ; Khairullah, Arican & Polat, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach

Alhudhaif

2021

PeerJ Computer Science

View full text Add to dashboard Cite

Background Brain signals (EEG—Electroencephalography) are a gold standard frequently used in epilepsy prediction. It is crucial to predict epilepsy, which is common in the community. Early diagnosis is essential to reduce the treatment process of the disease and to keep the process healthier. Methods In this study, a five-classes dataset was used: EEG signals from different individuals, healthy EEG signals from tumor document, EEG signal with epilepsy, EEG signal with eyes closed, and EEG signal with eyes open. Four different methods have been proposed to classify five classes of EEG signals. In the first approach, the EEG signal was first divided into four different bands (beta, alpha, theta, and delta), and then 25 time-domain features were extracted from each band, and the main EEG signal and these extracted features were combined to obtain 125-time domain features (feature extraction). Using the Random Forests classifier, EEG activities were classified into five classes. In the second approach, each One-Against-One (OVO) approach with 125 attributes was split into ten parts, pairwise, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the ten classifiers. In the third proposed method, each One-Against-All (OVA) approach with 125 attributes was divided into five parts, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the five classifiers. In the fourth proposed approach, each One-Against-All (OVA) approach with 125 attributes was divided into five parts. Since each piece obtained had an imbalanced data distribution, an adaptive synthetic (ADASYN) sampling approach was used to stabilize each piece. Then, each balanced piece was classified with the Random Forests classifier. To combine the decisions obtanied from each classifier, the majority voting scheme has been used. Results The first approach achieved 71.90% classification success in classifying five-class EEG signals. The second approach achieved a classification success of 91.08% in classifying five-class EEG signals. The third method achieved 89% success, while the fourth proposed approach achieved 91.72% success. The results obtained show that the proposed fourth approach (the combination of the ADASYN sampling approach and Random Forest Classifier) achieved the best success in classifying five class EEG signals. This proposed method could be used in the detection of epilepsy events in the EEG signals.

show abstract

Brain-computer interface speller system design from electroencephalogram signals with channel selection algorithms

Cited by 4 publications

References 25 publications

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach

Contact Info

Product

Resources

About