Marta Molinas

• Dr Eng.
• Professor (Full) at Norwegian University of Science and Technology

We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, we first use the discrete wavelet transform (DWT) or empirical mode decomposition (EMD) to decompose the EEG signals into a set...

Advances in machine learning (ML) and deep learning (DL) have led to automated sleep staging approaches that achieve high accuracy but often require extensive computational resources and/or high-density electroencephalograms (EEG). This paper presents a method for sleep staging using features extracted via the Discrete Wavelet Transform (DWT) and P...

In this paper, a fast state-of-charge balancing strategy for distributed energy storage system based on injected sinusoidal signals is proposed, which solves the problems of unbalanced state-of-charge, unreasonable load current sharing, and unstable direct current bus voltage. Firstly, the state-of-charge of distributed energy storage unit is direc...

The AppleCatcher game is a novel motor imagery brain-computer interface (BCI) designed with the idea to facilitate hand rehabilitation for individuals with motor impairments. It was developed to address the limitations of traditional methods by providing a more engaging and usercentered approach to rehabilitation. The system utilizes electroencepha...

Online small-signal stability analysis of wind farms is the key to activate preventive oscillation suppression measures. This paper proposes a near real-time poles estimation method for the online stability margin calculation via the Artificial Neural Network (ANN). First, based on the nodal admittance transfer function matrix of the wind farm, a n...

Diverse synchronization control dynamics, such as grid-following (GFL) and grid-forming (GFM) controls, are complicating the oscillatory behaviors in multi-converter systems they form. In this context, the impedance network (IN) based frequency-domain modal analysis (FMA) method is useful for diagnosing oscillations. However, since the adopted impe...

This study proposes a method to automatically identify dream experience (DE) and no experience (NE) during the sleep stage N2 of non-rapid eye movement (NREM). We investigated the use of machine learning (ML) to automatically identify when a subject is having a dream during NREM from electroencephalography (EEG) signals. We use permutation-based ch...

Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time-consuming and prone to bias. This study was aimed at developing an EEG-based machine learning (ML) model to automatically identify dream and dreamless s...

The current industry-proven Back Electromotive Force (back-EMF) based sensorless control frequently fails at low speeds and is also laborious to use. Interestingly in this work, these defects can be improved by developing a novel open loop back-EMF observer, where the sensorless drive could operate from 1% to 100% of the maximum speed, and it also...

Over the past century, Electroencephalography (EEG) technology has evolved from Hans Berger’s initial recordings with two electrodes to modern systems with over 300 electrodes. Recently, consumer-grade EEG devices with lower electrode density have emerged, laying the groundwork for EEG-based Brain-Computer Interfaces (BCIs) used in neurorehabilitat...

Abstract: Social drinking is a common activity among many people, but the effects of low doses of alcohol have not been as extensively researched as alcoholism or moderate drinking. Consuming one or two alcoholic drinks before driving can be dangerous and is illegal if the blood alcohol concentration (BAC) exceeds the legal limit, which varies betw...

The stability of the power grid with rich renewable energy resources is increasingly challenged, as power generation is highly intermittent and also with the mix of multiple energy vectors (e.g., power to hydrogen) of distinct dynamics. Furthermore, pre-designed stability margins considering certain operating points may be narrowed under varying co...

When the stability assessment of a power electronicdominated system is performed on a single coordinate, for example, through Bode plots in dd− and qq− axes, accuracy is compromised in frequency ranges where the frequency coupling is strong. Thus, the stability of such systems is normally analysed using a Multi-Input-Multi-Output (MIMO) framework,...

The increasing integration of renewable energy sources into power networks has elevated the risks of wideband oscillations (WBOs) in modern power systems. Unfortunately, the black/grey-box challenges inherent to renewable energy units (REUs) constrain our ability to effectively address these WBOs. Although impedance measurement or vector fitting me...

Today, alcohol drinking frequently accompanies socialising as a routine activity in various groups of society. 84.0% of individuals aged 18 and above in the United States have drunk alcohol at some point in their life (National Institute on Alcohol Abuse & US, 2023). Similarly, 81.7% of Norwegians in the age group 16 to 79 have drunk alcohol in 202...

Implementation aspects of fault-ride-through (FRT) operation in voltage source converter with virtual synchronous generator (VSG) control are discussed in this paper. The phenomenon of voltage decline during fault periods is recognized to be intricately associated with the interplay between power angle movement and the trajectory of current saturat...

In the emerging and fast-evolving applications such as renewables, energy storage, power grids and electrified transportation, power electronic converters are imposed with growing size and power capacity, continuously dropping price per kilowatt, more complicated control functions, and more complex working conditions. Under this background, it is e...

Equipping brain-computer interfaces with dream decoding capabilities could be vital in healthcare applications. We used high-density electroencephalogram data from non-rapid eye movement sleep to conduct qualitative analysis employing multivariate empirical mode decomposition and power spectral density (PSD) for preprocessing and machine learning a...

A high-performance real-time brain-computer interface system capable of identifying dreams has potential for healthcare applications. To address this, we use electroencephalogram (EEG) data from non-rapid eye movement sleep to classify dream experience and no-experience. Using 58 EEG channels, we achieve an accuracy of 0.94, an AUROC of 0.91, and a...

This presentation tackles the challenge of stability diagnosis in today's increasingly complex power systems. It covers three stability assessment tools, discussing their advantages and limitations, and advocates for the use of high-fidelity modeling combined with sensitivity analysis.

The power curve of a manufacturer must be compared with the actual power curve after commissioning because various factors lead to deviations. The main purpose of this study was to assess and compare the performance of the N72, N73, and N74 wind turbines of the Adama-II wind farm against the manufacturer's guaranteed power curve. The methods employ...

Dream decoders, communication systems for the handicap and motor imagery for neurorehabilitation are presented in the context of EEG-based Brain Computer Interfaces.

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EE...

Power system stability characteristics are typically evaluated in terms of small‐ and large‐signal (transient) stability. Access to the time‐varying A‐matrix of a state‐space‐based power systems model during transient conditions can be utilized to apply linear time‐varying system concepts for large‐signal stability analysis. In linear time‐varying...

We present a method that uses a convolutional neural network (CNN) called EEGNeX to extract and classify the characteristics of sleep-related waveforms from electroencephalo-graphic (EEG) signals in different stages of sleep. Our results showed that the CNN model with 128 channels achieved high performance, distributing the sleep stages into 2-clas...

This paper introduces a time–frequency analytic method for the exact identification of wideband DQ impedance models oriented to harmonic stability of converter-driven systems with transmission line models with distributed and frequency-dependent parameters. Wideband models are of great importance in the analysis of harmonic stability over a broad r...

This thesis investigates the feasibility of developing a real-time BCI communication system for patients with LIS using RGB-evoked EEG signals. The work compares various models on two datasets concerning classification accuracy. The two datasets used in the study are Dataset A, collected in Helsinki at Aalto University, and Dataset B, collected by...

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decompositio...

Couplings in frequency of the d-q impedance are quantified in the form of coupling strenght for the GFM and GFL converters. The coupling strength is used to evaluate a coupling factor that indicates the level of coupling introduced by different control blocks and control feedbacks. The coupling factor is then utilized to derive a corrective factor...

The journal entry investigates the feasibility of developing a real-time Brain-Computer Interface (BCI) communication system for patients with Locked-in Syndrome (LIS) using Red-Green-Blue (RGB) evoked Electroencephalography (EEG) signals. The work is based on two datasets: Dataset A, based on 21 subjects, where one of the colors red, green, or blu...

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the e...

Locked-in Syndrome (LIS) is a neurological condition that results in paralysis of the body and the loss of communication abilities while leaving the patient's cog-nitive function unaffected [Schnakers et al., 2008]. This can significantly impact their quality of life, as previously simple tasks become impossible. Developing a communication system b...

Distributed energy resources (DERs) are any energy resources in the electrical distribution systems, which can produce electricity, consume or store energy in a controlled manner, or be utilized to improve energy efficiency. They are typically smaller in scale than the traditional large-generation facilities. DERs include distributed generation uni...

A hundred years ago, with just two electrodes, Hans Berger recorded the first EEG signals and envisioned EEG as a "window into the brain." Since then, EEG technology has evolved to complete scalp coverage with over 300 electrodes and more recently shifting to a new trend, notably in emerging consumer-grade EEG systems, characterized by low electrod...

The work is done with the intention of developing a Brain-Computer-Interface (BCI) for communication for patients with Locked-in Syndrome (LIS), based on EEG signals evoked by RGB colors. This study investigates the differences in classification performance between models based on single individuals vs. general models, which are optimized on a test...

This talk takes you back to the 1920s where EEG finds its origins with the first human recordings done by Hans Berger in Germany. Starting with recordings from just two electrodes, EEG technology has undergone a remarkable transformation, now encompassing full scalp coverage with over 300 electrodes. However, recent years have witnessed a new trend...

The vector proportional-integral (PI)-controller is a widely-used control method for grid-connected voltage source converter (VSC); however, it has limitations that can negatively impact dynamic performance in a power electronics-dominated system. On the other hand, model predictive control (MPC) is proven advantageous; however, they require signif...

We explored the automatic classification of dreams with emotional content, which were collected by awakening 38 subjects after they had entered to Rapid Eye Movement (REM) sleep, and the dreams were recorded using 6 electroen-cephalographic (EEG) channels. We used the discrete wavelet transform for feature extraction and well-known classification a...

Demand response (DR) is an ancillary service that provides frequency support to the power grid. However, since its controller considers frequency deviations, the compensation provided by controllable loads such as thermostatically controllable loads (TCLs), may generate undesired harmonics into the power grid. This feature could be included into th...

Starting from its inception with recordings from just two electrodes, EEG technology has undergone a remarkable transformation, now encompassing full scalp coverage with over 300 electrodes. However, recent years have witnessed a new trend in emerging consumer-grade EEG systems and Brain-Computer Interfaces (BCI), characterized by low electrode den...

Five-phase permanent magnet synchronous motors (5Φ-PMSM) are demanded in many safe-crucial applications, for their appealing fault-tolerant capability. Unlike most previous articles on open-circuit faults (OCFs) and line-to-neutral faults, this paper concentrates on two-phase insulation faults, including the two-phase inter-turn fault (ITF) and the...

This paper presents a sleep versus wake-classification model based on high-density electroencephalographic (EEG) sleep data. In a second stage, an optimization algorithm is applied to select the minimal set of EEG channels according to their contribution to classification performance. The performance of these subsets are compared to the ones recomm...

This study aims to compare the automatic classification of emotions based on the self-reported level of arousal and valence with the Self-Assessment Manikin (SAM) when subjects were exposed to videos or images. The classification is performed on electroencephalographic (EEG) signals from the DEAP public dataset, and a dataset collected at the Unive...

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer-interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the e...

The chirp sweep is a go-to wide-band impedance measurement technique when speed and simplicity are of main concern. Additionally, the time-frequency trait of chirp scans provide unique benefits for systems exhibiting frequency couplings - a phenomenon often encountered in single-phase VSCs. Unfortunately, time domain interpretation of chirp respons...

This paper presents a sleep versus wake-classification model based on high-density electroencephalographic (EEG) sleep data. In a second stage, an optimization algorithm is applied to select the minimal set of EEG channels according to their contribution to classification performance. The performance of these subsets are compared to the ones recomm...

This study aims to compare the automatic classification of emotions based on the self-reported level of arousal and valence with the Self-Assessment Manikin (SAM) when subjects were exposed to videos or images. The classification is performed on electroencephalographic (EEG) signals from the DEAP public dataset, and a dataset collected at the Unive...

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer-interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the e...

The brain's response to visual stimuli of different colors might be used in a brain-computer interface (BCI) paradigm, for letting a user control their surroundings by looking at specific colors. Allowing the user to control certain elements in its environment, such as lighting and doors, by looking at corresponding signs of different colors could...

In recent times, we have seen extensive research in the field of EEG-based emotion identification. The majority of solutions suggested by current literature use sophisticated deep learning techniques for the identification of human emotions. These models are very complex and need huge resources to implement. Hence, in this work, a method for human...

This paper investigates the performance of automatic sleep stage classification with automatically selected features from electroencephalographic (EEG) signals using a Convolutional Neural Network (CNN) based on 5- and 2-class models. We defined two ways for 2-class stratification, to classify the sleep stages and compare its performance with predi...

Eigen-sensitivity is a useful tool in revealing the root cause of the oscillation instability and guiding its suppression. For the converters-dominated system, loop gain-based eigen-sensitivity (LGES) can be helpful, providing sensitivity information from the frequency domain perspective. However, the existing LGES is still scant in two aspects: ei...

Real-world experience on the problems faced by power systems with high power electronics penetration rates has been gathered from around the world over the past decade. And even if near 100% penetration will happen gradually, already today parts of a larger system could operate at near 100% penetration rate. In many systems around the world, oscill...

Power electronics systems are getting much more complex and the aggregated analysis method may not be effective in preserving crucial elements that are at the bottom of the instability. Networked analysis methods explored here are aimed to cover such complexity. Our and others’ works point towards addressing the complexity rather than simplifying t...

Classification of epileptic seizures based on electroencephalography (EEG) is a well-established research area. Most research uses a patient-dependent approach, i.e., training and test data come from the same patient. In order to use the results on other patients, a patient-independent model needs to be developed. The patient-independent model has...

The field of EEG-based emotion recognition has been widely explored in the last decade. Methods proposed by recent literature mostly use complex deep learning methods to achieve good predictions. To obtain comparable results using simpler machine learning techniques would be preferable as these models are much more intuitive to implement and unders...

The necessity of limiting the output current of grid-forming (GFM) voltage source converter (VSC) has led to drastic deformation of its power angle characteristic (PAC), posing a great challenge to the transient synchronization stability (TSS) analysis. This current limitation associated TSS issue has been extensively studied in the context of the...

EEG-based automatic emotion recognition using simple machine learning techniques on the DEAP and SEED datasets.

In modern power grids, many distributed generators (DGs) are connected via grid-connected inverters. Generally, the possibility of resonances among inverters and other equipment placed in proximity and the network itself must be analyzed in advance. However, it is often difficult to approach the problem for complex systems by using model-based tech...

p>The chirp sweep is a go-to wide-band impedance measurement technique when speed and simplicity are of main concern. Additionally, the time-frequency trait of chirp scans provide unique benefits for systems exhibiting frequency couplings - a phenomenon often encountered in single-phase VSCs. Unfortunately, time domain interpretation of chirp respo...

p>The chirp sweep is a go-to wide-band impedance measurement technique when speed and simplicity are of main concern. Additionally, the time-frequency trait of chirp scans provide unique benefits for systems exhibiting frequency couplings - a phenomenon often encountered in single-phase VSCs. Unfortunately, time domain interpretation of chirp respo...

Frequency-Domain (FD) modal method is a useful tool for oscillation analysis of converters-dominated systems. In this regard, the nodal admittance and loop-gain models are often applied, and both have their respective eigen-sensitivity-based Participation Factor (PF) analysis methods. Therefore, a question on how are they related or differed natura...

Coupling interaction within multi-source and multi-load (MSML) challenges the stable operation of DC microgrids. To disclose the essential mechanism, detailed small-signal model of the MSML DC microgrid is established first. Then, the relations between coupling parameters and stability margin are investigated using modal analysis and participation...

The phasor estimation process is important in monitoring, controlling and protecting power networks. Prony's method can be used as an estimator by which electrical power system parameters are approximated. The Prony algorithm is challenging under noise conditions due to its frequency-adaptive parameter estimations. Joining several channels of PMUs...

Early detection of Parkinson’s disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major arti...

The necessity of limiting the output current of grid-forming (GFM) voltage source converter (VSC) has led to drastic deformation of its power angle characteristic (PAC), posing a great challenge to the transient synchronization stability (TSS) analysis. This current limitation associated TSS issue has been extensively studied in the context of the...

Brain-computer interfaces (BCIs) require a set of defined brain activities to detect, each triggering a specific function in the computer. Such a set could be the brain's response to stimuli from the primary colors red, green, and blue (RGB). The goal of this project has been to develop intra-subject classifiers for classifying between RGB stimuli...

This work presents two approaches for epileptic seizure detection. One patient-independent and one patient-dependent approach. Feature and channel reduction was done on the patient-independent approach. An accuracy between 95.9% and 100% was obtained for the patient-dependent approach, depending on which machine learning method was used. An accurac...

Sleep quality highly influences all human beings' physical and mental health. Recent years have observed increased levels of sleep disorders, in addition to misdiagnosis. To allow for appropriate treatment , sleep monitoring and classification of sleep stages can assist with identifying specific sleep patterns and assessing sleep quality. Collectio...

Visual inspection of Polysomnography (PSG) recordings by sleep experts, based on established guidelines, has been the gold standard in sleep stage classification. This approach is expensive, time-consuming, and mostly limited to experimental research and clinical cases of major sleep disorders. Various automatic approaches to sleep scoring have bee...

With a history that started with recordings from two electrodes, EEG technology has evolved to a full coverage of the scalp with more than 300 electrodes, to only experience, in the last decades, a new trend of low electrode density in emerging consumer grade EEG systems. These new emerging EEG devices with low density electrodes are increasingly b...

Field-oriented control (FOC) still serves as an advanced way to control ac motors for industrial applications, as such, this technique is being expanded to the postfault operation of five-phase permanent magnet synchronous motors ( $5\Phi $ -PMSMs) under phase fault conditions. Previous articles have proved that the motor’s neutral voltage (NV) os...

The automatic distinction of human emotional states can provide the technological basis for applications in healthcare, education, marketing, and manufacturing sectors that rely on human-machine interfaces. Emotion recognition from Electroencephalography (EEG) signals is a challenging task entailing the development of classification models that sho...

The Impedance based stability analysis method is presented with examples of application in wind power.

Diverse synchronization dynamics within the grid-following (GFL)/grid-forming (GFM) converters-interlinked system are prone to induce oscillatory instabilities. To quantify their stability influences, frequency-domain modal analysis (FMA) method based on the impedance network can serve as a good reference. However, since the adopted impedance netwo...

Basic concepts of the Impedance-based stability analysis with introduction of three most basic AC impedance models. This presentation was part of the Tsukuba Innovation Arena (TIA) Power Electronics Summer School held at Tsukuba University with the support of AIST, KEK, and the University of Tsukuba Power Electronics Lab.

Marine sector decarbonization is another important battlefield for meeting the goal of climate action and ensuring the fulfillment of ambitions for a zero-emission society. Driven immediately by the policy incentives such as Energy Efficiency Design Index (EEDI) from International Maritime Organization(IMO), carbon taxation and labeling, a series o...