21 accepted papers
The deep neural networks are known to be vulnerable to well-designed adversarial attacks. The most successful defense technique based on adversarial training (AT) can achieve optimal robustness against particular attacks but cannot generalize well to unseen attacks. Another effective defense techniq…
Manual seizure detection from long recordings of the electroencephalogram (EEG) is a tiring, tedious, and error-prone process. It also requires experienced practitioners to detect seizure events precisely. This paper has proposed a novel method to detect epileptic seizures from long-EEG recordings u…
In numerous applications, binary reactions or event counts are observed and stored within high-order tensors. Tensor decompositions (TDs) serve as a powerful tool to handle such high-dimensional and sparse data. However, many traditional TDs are explicitly or implicitly designed based on the Gaussia…
Epileptic seizure detection from long recordings of scalp electroencephalography (EEG) is a challenging task owing to their unpredictability in nature with the inclusion of noise, artifacts and subject dependency. We hypothesize that selection of training EEG data plays important role in the model p…
This paper reports on a novel invasive brain–computer interface (BCI) paradigm that has successfully reconstructed spoken sentences from invasive electrocorticogram (ECoG) signals using deep-neural-network-based encoders and a pre-trained neural vocoder. We recorded ECoG signals while 13 participant…
Tensor decompositions (TDs) serve as a powerful tool for analyzing multiway data. Traditional TDs incorporate prior knowledge about the data into the model, such as a directed generative process from latent factors to observations. In practice, selecting proper structural or distributional assumptio…
Automated identification of epileptiform discharges in electroencephalograms (EEG) for the diagnosis of epilepsy can mitigate the burden of manual searches. Recent effective methods based on machine learning–based classification have used detection of candidate waveforms with signal processing and p…
Deep learning techniques are able to extract the characteristics of temporal signals to study their patterns and diagnose diseases such as essential tremor. However, these techniques require a large amount of data to train the neural network and achieve good results, and the more data the network ha…
Invasive brain–machine interfaces (BMIs) are a promising neurotechnological venture for achieving direct speech communication from a human brain, but it faces many challenges. In this paper, we measured the invasive electrocorticogram (ECoG) signals from seven participating epilepsy patients as they…
Epileptic focus localization in patients with epileptic seizures is essential when surgery is needed. Recent studies show that this can be done automatically using machine learning approaches. However, well-designed feature extraction methods are often computationally demanding, requiring a large am…
We propose ScalpNet: A deep neural network to detect spatiotemporal abnormal intervals from EEGs of epilepsy patients. Since the number of trained clinicians is very limited, it is very crucial to establish automatic detection of abnormal signals caused by epilepsy from EEGs. We build a convolutiona…
Epilepsy is a chronic disorder that causes unprovoked, recurrent-seizures. Characteristic spikes are often observed in the electroencephalogram (EEG) of epileptic patients in order to diagnose the disorder. Several methods have been investigated to automatically detect such spikes. The most common m…
This paper establishes an adaptive update method for the Gaussian kernel parameters in the application to the kernel adaptive filtering (KAF). In this method, the kernel parameters are all adaptive and data-driven, although they should be given or estimated by cross-validation. In terms of the Gauss…
Multiple stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, multiple visual stimuli are modulated with different properties such as frequencies and/or phases. However, the number of propert…
This paper addresses the problem of finding optimal sensor placement, i.e., determining F sensor positions from N possible locations. We propose a sensor selection method based on the localization operator of graph signal processing. This method can select sensors while considering the localizations…
Brain-computer interfacing (BCI) based on steady-state visual evoked potentials (SSVEPs) is one of the most promising techniques due to its high performance. A state-of-the-art is a BCI based on hybrid frequency and phase coded SSVEP, which needs a large set of calibration data as reference signals,…
We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the exist…
This paper presents a frequency recognition method of steady-state visual evoked potentials (SSVEPs) using binary subbands with canonical correlation analysis (CCA). The first subband contains all the target frequencies of SSVEPs. The second one includes the SSVEP signal corresponding to a desired n…
An adaptive online algorithm with a dictionary of observed signals for kernel principal subspace analysis is presented. A coefficient matrix for eigenfunctions is updated by a recursive least squares (RLS)-type algorithm and entries in the dictionary are adaptively added / removed preserving orthogo…
One of the major challenges in kernel adaptive filtering is how to construct an efficient dictionary of observed input signals. In this paper, we propose novel dictionary adaptation rules for kernel adaptive filtering. The first algorithm can efficiently “move” elements of the dictionary to increase…
An asynchronous brain-computer interface (BCI) is one of the crucial challenges in biomedical signal processing. In asynchronous BCIs, a state when a user does not intend to input commands needs to be distinguished from a state when he/she does. These states are called non-control (NC) state and int…