Use of EEG-Based Machine Learning to Predict Music-Related Brain Activity

Music has many awe-inspiring characteristics. Some may refer to it as a “universal language” with the ability to transcend the barriers of speech, while others may describe its ability to evoke intense emotional experiences for the listener. Regardless of the description, it is a commonly held view that music can have many profound effects. Studies of music’s effects have found these beliefs to be more than pure conjecture, finding that music interacts with and changes our brains in physical and emotional ways. Music can even have clinical applications, such as music therapy. This type of therapy has been shown to be beneficial in many areas, ranging from stroke rehabilitation to mental health treatment. The mechanisms behind music’s therapeutic benefit has to do with neuroplastic effects; Being able to harness this benefit in a therapeutic setting could make treatments for mental disorders and brain injuries even more effective. This thesis aimed to discover whether musical thoughts could be interpreted using machine learning, potentially opening the door to the use of thought-based musical training for therapeutic benefit. For this study, EEG data was collected while people were thinking of 5 melodies, then machine learning models were trained on labeled datasets. The models were then tasked with categorizing unlabeled sets of EEG data - in other words, predicting which melody a subject was thinking of while the data was being recorded. The accuracy of the predictions ranged from 45% to 80%, which means that the programs were 2-4 times more accurate than random guessing. This shows that these programs could potentially be used to examine the effects of musical thinking on neuroplasticity. While this topic is still exploratory and requires more research, these results could lead to a promising future of development of music-based brain-computer interfaces

Predicting and Composing a Top Ten Billboard Hot 100 Single with Descriptive Analytics and Classification

In late 20th and early 21st century Western popular music, there are cyclical structures, sounds, and themes that come and go with historical trends. Not only do the production techniques utilized reflect technological advancements (the Yamaha DX7, the Roland 808, etc.), the art form reflects contemporary cultural attitudes through lyrics and stylistic choice. Through this lens, pop songs can serve as historical artifacts for their unique ability to captivate listeners based on their generally acceptable and familiar elements, both upon release and with future audiences. It raises the questions: “Can a chronological analysis of artistic choices reveal trends in songwriting and popular music composition?”; “Based on collected analysis, could forecast data suggest criteria that a future hit song may fit?”; and “How could the next ‘hit song’ sound, based on the calculated criteria from trend analysis and forecasting techniques?” By manually listening to and analyzing Billboard songs for each of the last 50 years and employing an assortment of feature selection and classification techniques, a random forest model predicts some of the significant characteristics of a potential future hit song. This prediction provided the framework for an original composition

Deep Multi Temporal Scale Networks for Human Motion Analysis

The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder. The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task. Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected. Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property. In this thesis, we will focus on the analysis of human movement using data-driven techniques. In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements. The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed). Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss. The literature on data-driven models proposes two families of methods that can process time series and human movement. The first family, called shallow models, extract features from the time series that can help the learning algorithm find associations in the data. These features are identified and designed by domain experts who can identify the best ones for the problem faced. On the other hand, the second family avoids this phase of extraction by the human expert since the models themselves can identify the best set of features to optimise the learning of the model. In this thesis, we will provide a method that can apply the multi-temporal scales property of the human motion domain to deep learning models, the only data-driven models that can be extended to handle this property. We will ask ourselves two questions: what happens if we apply knowledge about how human movements are performed to deep learning models? Can this knowledge improve current automatic recognition standards? In order to prove the validity of our study, we collected data and tested our hypothesis in specially designed experiments. Results support both the proposal and the need for the use of deep multi-scale models as a tool to better understand human movement and its multiple time-scale nature

Transforming Time Series for Efficient and Accurate Classification

Time series data refer to sequences of data that are ordered either temporally, spatially or in another defined order. They can be frequently found in a variety of domains, including financial data analysis, medical and health monitoring and industrial automation applications. Due to their abundance and wide application scenarios, there has been an increasing need for efficient machine learning algorithms to extract information and build knowledge from these data. One of the major tasks in time series mining is time series classification (TSC), which consists of applying a learning algorithm on labeled data to train a model that will then be used to predict the classes of samples from an unlabeled data set. Due to the sequential characteristic of time series data, state-of-the-art classification algorithms (such as SVM and Random Forest) that performs well for generic data are usually not suitable for TSC. In order to improve the performance of TSC tasks, this dissertation proposes different methods to transform time series data for a better feature extraction process as well as novel algorithms to achieve better classification performance in terms of computation efficiency and classification accuracy. In the first part of this dissertation, we conduct a large scale empirical study that takes advantage of discrete wavelet transform (DWT) for time series dimensionality reduction. We first transform real-valued time series data using different families of DWT. Then we apply dynamic time warping (DTW)-based 1NN classification on 39 datasets and find out that existing DWT-based lossy compression approaches can help to overcome the challenges of storage and computation time. Furthermore, we provide assurances to practitioners by empirically showing, with various datasets and with several DWT approaches, that TSC algorithms yield similar accuracy on both compressed (i.e., approximated) and raw time series data. We also show that, in some datasets, wavelets may actually help in reducing noisy variations which deteriorate the performance of TSC tasks. In a few cases, we note that the residual details/noises from compression are more useful for recognizing data patterns. In the second part, we propose a language model-based approach for TSC named Domain Series Corpus (DSCo), in order to take advantage of mature techniques from both time series mining and Natural Language Processing (NLP) communities. After transforming real-valued time series into texts using Symbolic Aggregate approXimation (SAX), we build per-class language models (unigrams and bigrams) from these symbolized text corpora. To classify unlabeled samples, we compute the fitness of each symbolized sample against all per-class models and choose the class represented by the model with the best fitness score. Through extensive experiments on an open dataset archive, we demonstrate that DSCo performs similarly to approaches working with original uncompressed numeric data. We further propose DSCo-NG to improve the computation efficiency and classification accuracy of DSCo. In contrast to DSCo where we try to find the best way to recursively segment time series, DSCo-NG breaks time series into smaller segments of the same size, this simplification also leads to simplified language model inference in the training phase and slightly higher classification accuracy. The third part of this dissertation presents a multiscale visibility graph representation for time series as well as feature extraction methods for TSC, so that both global and local features are fully extracted from time series data. Unlike traditional TSC approaches that seek to find global similarities in time series databases (e.g., 1NN-DTW) or methods specializing in locating local patterns/subsequences (e.g., shapelets), we extract solely statistical features from graphs that are generated from time series. Specifically, we augment time series by means of their multiscale approximations, which are further transformed into a set of visibility graphs. After extracting probability distributions of small motifs, density, assortativity, etc., these features are used for building highly accurate classification models using generic classifiers (e.g., Support Vector Machine and eXtreme Gradient Boosting). Based on extensive experiments on a large number of open datasets and comparison with five state-of-the-art TSC algorithms, our approach is shown to be both accurate and efficient: it is more accurate than Learning Shapelets and at the same time faster than Fast Shapelets. Finally, we list a few industrial applications that relevant to our research work, including Non-Intrusive Load Monitoring as well as anomaly detection and visualization by means for hierarchical clustering for time series data. In summary, this dissertation explores different possibilities to improve the efficiency and accuracy of TSC algorithms. To that end, we employ a range of techniques including wavelet transforms, symbolic approximations, language models and graph mining algorithms. We experiment and evaluate our approaches using publicly available time series datasets. Comparison with the state-of-the-art shows that the approaches developed in this dissertation perform well, and contribute to advance the field of TSC

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Proc. 33. Workshop Computational Intelligence, Berlin, 23.-24.11.2023

Dieser Tagungsband enthält die Beiträge des 33. Workshops „Computational Intelligence“ der vom 23.11. – 24.11.2023 in Berlin stattfindet. Die Schwerpunkte sind Methoden, Anwendungen und Tools für ° Fuzzy-Systeme, ° Künstliche Neuronale Netze, ° Evolutionäre Algorithmen und ° Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen.The workshop proceedings contain the contributions of the 33rd workshop "Computational Intelligence" which will take place from 23.11. - 24.11.2023 in Berlin. The focus is on methods, applications and tools for ° Fuzzy systems, ° Artificial Neural Networks, ° Evolutionary algorithms and ° Data mining methods as well as the comparison of methods on the basis of industrial and benchmark problems

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio