A large-scale evaluation framework for EEG deep learning architectures

EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently emerging EEG decoding approach is deep learning with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many different architectures already published. Here we present a novel framework for the large-scale evaluation of different deep-learning architectures on different EEG datasets. This framework comprises (i) a collection of EEG datasets currently including 100 examples (recording sessions) from six different classification problems, (ii) a collection of different EEG decoding algorithms, and (iii) a wrapper linking the decoders to the data as well as handling structured documentation of all settings and (hyper-) parameters and statistics, designed to ensure transparency and reproducibility. As an applications example we used our framework by comparing three publicly available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow ConvNet, and two versions of EEGNet. We also show how our framework can be used to study similarities and differences in the performance of different decoding methods across tasks. We argue that the deep learning EEG framework as described here could help to tap the full potential of deep learning for BCI applications.Comment: 7 pages, 3 figures, final version accepted for presentation at IEEE SMC 2018 conferenc

Formation of visual memories controlled by gamma power phase-locked to alpha oscillations

Neuronal oscillations provide a window for understanding the brain dynamics that organize the flow of information from sensory to memory areas. While it has been suggested that gamma power reflects feedforward processing and alpha oscillations feedback control, it remains unknown how these oscillations dynamically interact. Magnetoencephalography (MEG) data was acquired from healthy subjects who were cued to either remember or not remember presented pictures. Our analysis revealed that in anticipation of a picture to be remembered, alpha power decreased while the cross-frequency coupling between gamma power and alpha phase increased. A measure of directionality between alpha phase and gamma power predicted individual ability to encode memory: stronger control of alpha phase over gamma power was associated with better memory. These findings demonstrate that encoding of visual information is reflected by a state determined by the interaction between alpha and gamma activity

비침습적 뇌파 신호를 이용한 응급환자의 생체반응 모니터링 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2021. 2. 김희찬.뇌파는 대뇌피질이나 두피의 전극을 통해서 뇌의 전기적 신호를 기록한 것을 의미한다. 뇌 기능 관찰을 위한 진단도구로써 뇌파는 뇌전증이나 치매 진단 등의 목적으로 활용되고 있다. 본 논문에서는 비침습적 뇌파를 이용하여 응급환자의 주요 생리학적 지표를 모니터링하는 기술을 개발하였다. 처음 두 연구에서 심폐소생술의 효과를 평가하기 위한 심정지 돼지실험모델을 고안하였다. 현재의 심폐소생술 지침은 체순환 평가를 위해 기도삽관을 통한 호기말 이산화탄소 분압의 측정을 권고한다. 하지만, 정확한 기도삽관이 특히 병원 밖 상황에서 어려울 수 있다. 따라서, 간편히 측정할 수 있고 소생 환자의 신경학적 예후를 진단하는데 사용되는 뇌파를 이용한 예측 기술이 심폐소생술 품질평가지표의 대안으로 제안되었다. 첫 번째 실험에서는 고품질과 저품질 기본심폐소생술을 10회 반복하면서 측정된 뇌파를 분석하였다. 심폐소생술의 품질에 따른 뇌파의 변화를 이용하여 체순환 평가를 위한 EEG-based Brain Resuscitation Index (EBRI) 모델을 도출하였다. EBRI 모델에서 획득한 호기말 이산화탄소 분압 예측치는 실제 값과 양의 상관관계를 보이며, 병원 밖 상황에서의 활용 가능성을 보였다. 두 번째 실험에서는 두 가지 심폐소생술(기본심폐소생술, 전문심폐소생술)이 수행되었다. 제세동 직전에 수집된 뇌파는 심폐소생술 도중 경동맥혈류의 회복률과 함께 분석되었다. 심폐소생술 도중 경동맥혈류의 회복률을 반영하는 뇌파 변수를 규명한 후, 이를 이용하여 높은 회복률(30% 이상)과 낮은 회복률(30% 미만)을 구분하는 기계학습 기반 이진분류모델을 도출하였다. 서포트 벡터 머신 기반의 예측모델이 0.853의 정확도와 0.909의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 이러한 예측모델은 심정지 환자의 뇌 소생을 향상시켜 빠른 뇌 기능 회복을 가능하게 할 것으로 기대된다. 세 번째 연구에서 비침습적 뇌파를 이용하여 두개내압을 예측하는 모델을 개발하기 위한 외상성 뇌손상 돼지실험모델이 고안되었다. 외상성 뇌손상은 물리적 충격에 의해 정상적인 뇌 기능이 중단된 상태를 의미하며, 이 때의 두개내압 상승과 관류저하가 뇌파에 영향을 끼칠 수 있다. 따라서, 우리는 뇌파 기반 두개내압 예측모델을 개발하였다. 폴리카테터로 실험동물의 두개내압을 변경하면서 뇌파를 획득하였다. 두개내압의 정상구간(25 mmHg 미만)과 위험구간(25 mmHg 이상)을 유의미하게 구분하는 뇌파 변수를 규명한 후 기계학습 기반 이진분류모델을 도출하였다. 다층 퍼셉트론 기반의 예측모델이 0.686의 정확도와 0.754의 곡선하면적을 보이며 가장 우수한 성능을 보였다. 또다른 비침습 데이터인 심박수 정보와 함께 사용하였을 때 정확도와 곡선하면적은 각각 0.760과 0.834로 향상되었다. 제안된 예측모델은 응급상황에서 비침습적으로 두개내압을 관찰하여 정상 수준의 두개내압을 유지하는데 도움을 줄 것으로 기대된다. 본 논문은 응급환자의 주요 생리학적 지표를 비침습적 뇌파를 이용하여 관찰하는 예측모델을 제안하고 성능을 검증하였다. 본 연구에서는 뇌파를 이용하여 즉각적인 호기말 이산화탄소 분압, 경동맥혈류, 두개내압을 추정하기 위한 예측모델을 수립하였다. 하지만, 뇌파 데이터는 장기간의 신경학적, 기능적 회복과 함께 평가되어야 한다. 본 논문에서 개발한 예측모델의 성능과 적용 가능성은 향후 다양한 임상연구를 통해 cerebral performance category와 modified Rankin scale 등의 신경학적 평가지표와 함께 분석, 개선되어야 할 것이다.Electroencephalogram (EEG) is a recording of the electrical activity of the brain, measured using electrodes attached to the cerebrum cortex or the scalp. As a diagnostic tool for brain disorders, EEG has been widely used for clinical purposes such as epilepsy- and dementia diagnosis. This study develops an EEG-based noninvasive critical care monitoring method for emergency patients. In the first two studies, ventricular fibrillation swine models were designed to develop EEG-based monitoring methods for evaluating the effectiveness of cardiopulmonary resuscitation (CPR). The CPR guidelines recommend measuring end-tidal carbon dioxide (ETCO2) via endotracheal intubation to assess systemic circulation. However, accurate insertion of the endotracheal tube might be difficult in an out-of-hospital setting (OOHS). Therefore, an easily measurable EEG, which has been used to predict resuscitated patients neurologic prognosis, was suggested as a surrogate indicator for CPR feedback. In the first experimental setup, the high- and low quality CPRs were altered 10 times repeatedly, and the EEG parameters were analyzed. Linear regression of an EEG-based brain resuscitation index (EBRI) was used to estimate ETCO2 levels as a novel feedback indicator of systemic circulation during CPR. A positive correlation was found between the EBRI and the real ETCO2, which indicates the feasibility of EBRI in OOHSs. In the second experimental setup, two types of CPR mode were performed: basic life support and advanced cardiovascular life support. EEG signals that were measured between chest compressions and defibrillation shocks were analyzed to monitor the cerebral circulation with respect to the recovery of carotid blood flow (CaBF) during CPR. Significant EEG parameters were identified to represent the CaBF recovery, and machine learning (ML)-based classification models were established to differentiate between the higher (≥ 30%) and lower (< 30%) CaBF recovery. The prediction model based on the support vector machine (SVM) showed the best performance, with an accuracy of 0.853 and an area under the curve (AUC) of 0.909. The proposed models are expected to guide better cerebral resuscitation and enable early recovery of brain function. In the third study, a swine model of traumatic brain injury (TBI) was designed to develop an EEG-based prediction model of an elevated intracranial pressure (ICP). TBI is defined as the disruption of normal brain function due to physical impact. This can increase ICP, and the resulting hypoperfusion can affect the cerebral electrical activity. Thus, we developed EEG-based prediction models to monitor ICP levels. During the experiments, EEG was measured while the ICP was adjusted with the Foley balloon catheter. Significant EEG parameters were determined to differentiate between the normal (< 25 mmHg) and dangerous (≥ 25 mmHg) ICP levels and ML-based binary classifiers were established to distinguish between these two groups. The multilayer perceptron model showed the best performance with an accuracy of 0.686 and an AUC of 0.754, which were improved to 0.760 and 0.834, respectively, when a noninvasive heart rate was also used as an input. The proposed prediction models are expected to instantly treat an elevated ICP (≥ 25 mmHg) in emergency settings. This study presents a new EEG-based noninvasive monitoring method of the physiologic parameters of emergency patients, especially in an OOHS, and evaluates the performance of the proposed models. In this study, EEG was analyzed to predict immediate ETCO2, CaBF, and ICP. The prediction models demonstrate that a noninvasive EEG can yield clinically important predictive outcomes. Eventually, the EEG parameters should be investigated with regard to the long-term neurological and functional outcomes. Further clinical trials are warranted to improve and evaluate the feasibility of the proposed method with respect to the neurological evaluation scores, such as the cerebral performance category and modified Rankin scale.Abstract i Contents iv List of Tables viii List of Figures x List of Abbreviations xii Chapter 1 General Introduction 1 1.1 Electroencephalogram 1 1.2 Clinical use of spontaneous EEG 5 1.3 EEG and cerebral hemodynamics 7 1.4 EEG use in emergency settings 9 1.5 Noninvasive CPR assessment 10 1.6 Noninvasive traumatic brain injury assessment 16 1.7 Thesis objectives 21 Chapter 2 EEG-based Brain Resuscitation Index for Monitoring Systemic Circulation During CPR 23 2.1 Introduction 23 2.2 Methods 25 2.2.1 Ethical statement 25 2.2.2 Study design and setting 25 2.2.3 Experimental animals and housing 27 2.2.4 Surgical preparation and hemodynamic measurements 27 2.2.5 EEG measurement 29 2.2.6 Data analysis 32 2.2.7 EBRI calculation 33 2.2.8 Delta-EBRI calculation 34 2.3 Results 36 2.3.1 Hemodynamic parameters 36 2.3.2 Changes in EEG parameters 37 2.3.3 EBRI calculation 39 2.3.4 Delta-EBRI calculation 41 2.4 Discussion 42 2.4.1 Accomplishment 42 2.4.2 Limitations 45 2.5 Conclusion 46 Chapter 3 EEG-based Prediction Model of the Recovery of Carotid Blood Flow for Monitoring Cerebral Circulation During CPR 47 3.1 Introduction 47 3.2 Methods 50 3.2.1 Ethical statement 50 3.2.2 Study design and setting 50 3.2.3 Experimental animals and housing 52 3.2.4 Surgical preparation and hemodynamic measurements 54 3.2.5 EEG measurement 55 3.2.6 Data processing 57 3.2.7 Data analysis 58 3.2.8 Development of machine-learning based prediction model 59 3.3 Results 63 3.3.1 Results of CPR process 63 3.3.2 EEG changes with the recovery of CaBF 66 3.3.3 Changes in EEG parameters depending on four CaBF groups 68 3.3.4 Changes in EEG parameters depending on two CaBF groups 69 3.3.5 EEG parameters for prediction models 70 3.3.6 Performances of prediction models 73 3.4 Discussion 76 3.4.1 Accomplishment 76 3.4.2 Limitations 78 3.5 Conclusion 80 Chapter 4 EEG-based Prediction Model of an Increased Intra-Cranial Pressure for TBI patients 81 4.1 Introduction 81 4.2 Methods 83 4.2.1 Ethical statement 83 4.2.2 Study design and setting 83 4.2.3 Experimental animals and housing 85 4.2.4 Surgical preparation and hemodynamic measurements 86 4.2.5 EEG measurement 88 4.2.6 Data processing 90 4.2.7 Data analysis 90 4.2.8 Development of machine-learning based prediction model 91 4.3 Results 92 4.3.1 Hemodynamic changes during brain injury phase 92 4.3.2 EEG changes with an increase of ICP 93 4.3.3 EEG parameters for prediction models 94 4.3.4 Performances for prediction models 95 4.4 Discussion 100 4.4.1 Accomplishment 100 4.4.2 Limitations 104 4.5 Conclusion 104 Chapter 5 Summary and Future works 105 5.1 Thesis summary and contributions 105 5.2 Future direction 108 Bibilography 113 Abstract in Korean 135Docto

Using Brain–Computer Interfaces and Brain-State Dependent Stimulation as Tools in Cognitive Neuroscience

Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work

Dysfunction of neurovascular/metabolic coupling in chronic focal epilepsy

In this study, we aim to evaluate the mechanisms underlying the neuro-vascular/metabolic coupling in the epileptogenic cortices of rats with chronic focal epilepsy. To that end, we first analyzed intracranial recordings (electrophysiology, laser Doppler flowmetry and optical imaging) obtained from the seizure onset zones during ictal periods and then used these data to fit a metabolically-coupled balloon model. This biophysical model is an extension of the standard balloon model with modulatory effects of changes in tissue oxygenation, capillary dynamics and variable O2 extraction fraction. As previously reported using acute seizure models, we found that there is a significant higher contribution from high local field potential frequency bands to the cerebral blood flow (CBF) responses in the epileptogenic cortices during ictal neuronal activities. The hemodynamic responses associated with ictal activities were distance-dependent with regard to the seizure focus, though varied in profiles from those obtained from acute seizure models. Parameters linking the CBF and relative concentration of deoxy-hemoglobin to neuronal activity in the biophysical model were significantly different between epileptic and normal rats. In particular, we found that the coefficient associated with the strength of the functional hyperemic response was significantly larger in the epileptogenic cortices, although changes in hemoglobin concentration associated with ictal activity reflected the existence of a significantly higher baseline for oxygen metabolism in the epileptogenic cortices