Abstract

Syntactic parsing in NLP is the task of working out the grammatical structure of sentences. Some of the purely formal approaches to parsing such as phrase structure grammar, dependency grammar have been successfully employed for a variety of languages. While phrase structure based constituent analysis is possible for fixed order languages such as English, dependency analysis between the grammatical units have been suitable for many free word order languages such as Indian languages. All these parsing approaches rely on identifying the linguistic units based on their formal syntactic properties and establishing the relationships between such units in the form of a tree. Dravidian languages which are spoken in Southern India are morphologically-rich, agglutinative languages whose characterization on purely structural terms such as adjectives, adverbs, conjunctions, postpositions as well as traditional interpretations of tense and finiteness pose problems in their syntactic analysis which are well-discussed in literature. We propose that the morpho-syntactic structures of Dravidian languages are better analysed from the theoretical perspectives of “Cognitive Grammar” or “Construction Grammar” where every grammatical structure is treated as a symbol that directly maps to meaningful conceptualizations. In other words, natural language is not treated as a formal system but as a functional system that is entirely symbolic or semiotic right from lexicon to grammar. Through linguistic evidences we point out that morpho-syntactic structures in Dravidian languages have their basis in meaningful discourse conceptualizations. Subsequently we hierarchically arrange all these conceptualizations into construction schemas that exhibit multiple-inheritance relationships and we explain all concrete morpho-syntactic structures as instances of these schemas. Based on this fresh theoretical grounding, we model parsing as automatic identification of meaningful dependency relations between such meaningful construction units. We formulated an annotation scheme for labelling the construction units and dependency relations that can exist between these units. Our approach to full parser annotation shows an average MALT LAS of 82.21% on Tamil gold annotated corpus of 935 sentences in a five-fold validation experiment. We conducted experiments by varying training data size, annotation scheme, length of a sentence in terms of number of chunks, granularity of tags and report the parser results of these scenarios. Finally, we build a pipeline with splitter, construction labeller, grouper as intermediate layers before MALT parser input and release the working full parser module.

Abstract

Complex tasks are often handled through software implementation in combination with high per- formance processors. Taking advantage of hardware parallelism, FPGA is breaking the paradigm by accomplishing more per clock cycle with closely matched application requirements. With the aim to minimise computation delay with increase in map size and geometric constraints, FPGA based mas- sively parallel architectures are designed for super fast path planning via Rapidly Exploring Random Trees(RRT) algorithm. A Rapidly Exploring Random Tree (RRT) is an algorithm designed to efficiently search non-convex, high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally using samples drawn randomly from the search space. RRTs can be viewed as a technique to gener- ate open-loop trajectories for nonlinear systems with state constraints. But the algorithm is weighed down by extravagant waiting times in computation to completion, owing to its probabilistically random searching of the environment. Hence, the RRT algorithm can consume precious time cycles in generat- ing a solution to the problem of exploring a given environment. Efforts to ameliorate the unsatisfactory exploration time have focused on two areas : a.) Algorithmic design improvements b.) Parallelizing the algorithm. While Algorithmic design changes such as biased exploration, controlled exploration, faster nearest neighbour search have minimised the exploration times, the most breakthrough improve- ments have been achieved by attempts at parallelizing RRT. Here, parallelizing RRT refers to the design philosophy of integrating multiple RRT modules into an architecture that allows each of the RRT mod- ule to work in parallel while also enabling mechanisms for all of the RRT modules to collaborate and cooperate to achieve faster exploration of the environment. In the field of robotics, an FPGA is capable of delivering tightly packed, energy efficient infrastruc- tures adept in fast real time performance. An FPGA allows gate level control of system architecture. This allows the designer to tap the potential of hardware design, allowing control over minute details of arithmetic design, real time parallelization, pipe-lining of sequential processes. The hardware level flexibility afforded by an FPGA results in designs that are not only fast, but also small and power ef- ficient. FPGA boards generally consume small physical area, making it an ideal solution for robotic systems with constrained dimensions. FPGA implementation of Image processing descriptors, Random Decision Tree Body Part Recognition Using FPGA and FPGA based collision avoidance further validate this belief. This work presents two FPGA based massively parallel architectures : 1.) Hierarchical and 2.) Com- binatorial. These architectures are also analyzed against hardware implementations of other scalable RRT methods for motion planning, mainly Distributed RRT and K-Distributed RRT. Notable further- ance of acceleration capabilities was observed with the proposed architectures delivering a sizable gain over the other implementations while maintaining uniformity in exploration. To further quantify the rea- soning behind using FPGAs as the choice of parallel platform, quantitative analysis of the performance metrics of the RRT algorithm across numerous embedded hardware solutions was performed. It was ob- served that FPGA enables hardware level optimizations that offer real time performance and economical power consumption levels. Further, to validate the empirical functioning of the architectures in a real world scenario, a Zynq-SoC based differential drive robotic system was developed as a test platform to run the architectures in real time in a complex environment filled with obstacles. In summary, the designed architectures, proven adept at running in real-time on a robot, delivered a sizable gain over other state of the art algorithms, while maintaining critical parameters of the exploration like uniformity and density and also maintaining critical mobile robot requirements of small size and ambivalent power consumption levels.

Abstract

The Web 2.0, through its different platforms such as blogs, social networks, microblogs, or forums allows users to freely write content on the Internet, with the purpose to provide, share and use information. It is known that these type of platforms are among the top visited websites1, and their interest is growing more and more. Given the important role that social media usage is increasingly playing in daily life, a growing body of literature has emerged in the research community that aims to mine social media content, or to evaluate the linguistic aspects of that content in order to better understand its dynamics (Golder et al., 2011) [22]. This user-generated content has a huge drawback, which is the informal nature of the language used. Non-standard abbreviations, contracted spelling variations, casual grammatical structure are just some of the aspects of social media language. Over the past few decades, sociolinguists have been interested in a phenomena called “code mixing”, which has been observed in social media data. Code-Mixing refers to the embedding of linguistic units such as phrases, words or morphemes of one language into an utterance of another language. It is frequently seen in user generated content on social media like Facebook and Twitter, especially by multilingual users. Apart from the inherent linguistic complexity, the analysis of code-mixed content poses complex challenges owing to the presence of spelling variations, transliteration and non-adherence to a formal grammar. Due to the presence of such data all across social media, there is also a need to understand it. For any downstream Natural Language Processing task, tools that are able to process and analyse codemixed data are required. The first steps to understanding this data are language identification and word normalisation systems, so that we obtain the standard form of a crude sentence from social media. In this thesis, we have developed a system for language identification and word normalisation for Hindi-English code-mixed social media text (CMST). We have provided annotation guidelines for our system, after analysing the complex nature of the dataset used. Using this system, we have released a dataset of 1446 code-mixed Hindi-English sentences along with the associated language and normalisation labels. To the best of our knowledge, our work is the first attempt at the creation of an annotated linguistic resource for this language pair, which is also made public. We have also performed experiments with shallow parsing, in an attempt to build a complete pipeline from raw data to shallow parsed data. Our pipeline consists of 4 modules - Language Identification, Normalisation, POS Tagging, and Shallow Parsing. As far as we understand, we are the first to attempt shallow parsing on code-mixed social media text. This system has been released online.

Abstract

Designing embedded systems efficiently has always been of significant interest. This is primarily motivated by the fact that these embedded systems find application in a wide range of fields: industrial automation, commercial electronics, scientific and medical applications, and many more. With the inherent drive towards automation in today’s world, these applications are only ever-increasing. With the tremendous rise in the complexity of such systems and the need to satisfy multiple and mutually conflicting constraints, the design optimization problem has become extremely large and complex. Hence, there is an urgent necessity to develop new and efficient techniques for design of embedded systems. In this thesis, we apply and refine the design strategy of Hardware Software Partitioning (HW/SW Partitioning) to embedded systems. In the first part of the thesis, HW/SW Partitioning is introduced and then, a co-design methodology is utilized to enhance the widely used Fast Fourier Transform algorithm. A high performance and low-cost implementation is proposed which exploits the principles of Partitioning on a target system consisting of a Spartan 3E FPGA and an Atmega-16 micro-controller. A comparative analysis is then demonstrated, with respect to both, the hardware-only and software-only implementations, which depict the superior performance of the proposed implementation. It is then observed that as the problem size, i.e. complexity of target application/algorithm increased, making Partitioning decisions manually was not feasible. Hence, in order to utilize the true potential of HW/SW Partitioning (especially for large-scale applications), automation of the partitioning process was required. This formed the next focus of this thesis, which comprised of developing high speed and resource efficient algorithms for the problem of HW/SW Partitioning. The problem has been modeled as a multi-dimensional optimization problem with the aim of minimizing the area utilization, power dissipation, time of execution and system memory requirement of the implementation. The novelty of this work is in developing a fast paced, state-of-the-art algorithm which takes into consideration all the major design costs as mentioned above. The Greedy Metaheuristic Algorithm (GMA), with O(n) time complexity has been proposed, which significantly reduces the complexity of algorithms previously described in the literature. The proposed approach can also be efficiently implemented on resource constraint devices; such as low cost System On Chips (SoCs). The problem model being considered thus far was lacking one major factor, the communication costs incurred by the different hardware and software processing engines (PEs). With the contemporary age of Multi-Processor System on Chips (MPSoCs) and hybrid multi-core systems, this became a very important cost which could not be neglected. Thus, in this work, an extension to the previous model was introduced next. Subsequently, a two-phased algorithm, called Phased Greedy Metaheuristic Algorithm (PGMA) was proposed, which solved the problem of Partitioning, considering all the relevant communication costs. In order to evaluate the efficiency and performance of the proposed algorithms, a detailed empirical study was also performed. A comparison of the execution time and solution quality of the proposed algorithms with other major partitioning algorithms in the literature was depicted. The time of execution was found to be as low as 0.011 seconds, with deviation from optimal solution by less than 2% (solution error of under 2%), for partitioning an algorithm consisting of 2500 blocks. To the best of our knowledge, this is the fastest performance till date. Due to the heuristic nature of the algorithms, it was necessary to ascertain the solution quality (how optimal were the generated solutions) and compare them with other approaches. This analysis also confirmed the usefulness of the proposed algorithms. After an intensive theoretical design and performance evaluation, it became imminent to apply the proposed algorithms to real life embedded systems and determine their effectiveness. A comprehensive literature survey was conducted to find a suitable problem of relevance to HW/SW Partitioning. With the growing use and reach of digital photography, embedded systems have been increasingly used in the area of digital signal processing. Joint-Photographic Expert-Group (JPEG) Encoder was one such contemporary embedded system that is widely used in numerous applications. This has resulted in a demand for improved and more efficient constructions of the system. Any research carried out to improve the construction of JPEG encoder will have a significant impact in enhancing contemporary embedded system research. Thus, for this thesis, we chose to propose an efficient partitioning scheme for the JPEG encoder, using the proposed algorithm PGMA. A real-life case study was performed. Th

Abstract

In today’s era social media is our window into creating contextual systems of advanced technology. These systems span a breadth of industries and domains. Whether it is contextual phone assistants like Siri and Crotana or search based advertisements, all of them rely deeply on user data to teach themselves human-like responsiveness. A major chunk of the data available at our disposal is lexical data (primarily from social media platforms). Thus, psychology infered via lexical markers online is an indispensable part of the future of our technology. Lexical psychology and personality are paradigms that have been given immense attention for over a decade. Most state of the art techniques use pre-existing features from social media functions (for instance Number of followers , Likes on Facebook etc. to predict various personality models (such as Big Five , MBTI etc.) that have been around for over 70 years. Parts of our work attempt to tackle this approach of using pre-existing features (available for direct reference from social media). In doing so, we traced the conception of the Big Five model of personality to be concieved by means of extensive lexical analysis (both empirical and theoretical). Based on this knowledge, we illustrate the importance of additional features (informed by the fundamentals of psychology) in training robust personality pre- diction models. Training one such model ourselves, the results we achieved were a clear indicator of the effectiveness of lexical features versus stand-alone technological features to predict user personality. We then move on to tackle the drawbacks of the continued usage of the Big Five model to understand user personality since the last 70 years. At the time this model was proposed by Goldberg et al. the only mode for obtaining relevant user data was by means of psychometric tests. While the databases available to us have advanced significantly, the models used to capture lexical personality from these data reserves remain constant. They make available to us an overall persona of an individual. Contrary to these, abundant literature in the domain of psychology suggests that the persona of an individual is ever evolving. It is dynamic and changes temporally with the individual’s cognition, action and experiences. As opposed to this our technology today is still dependent on such overall personality models (static personality traits from Big Five and other’s of it’s likes). We have by means of this study attempted to discover, formulate and compute a dynamic model of representing the persona of an individual to overcome these limitations. In each of the domains described above an iterative model will elevate the contextual information by a manifold as opposed to the approximate static summarization of an individual’s persona. Thus, inspired by the Abhidhamma scholarship of Buddhism, we proposed PACMAN - the psycho computational model of an individual. The model theoretically represents a stochastic finite state ma- chine that encapsulates the moment by moment mental states of an individual. It is empowered by its genesis which lies in the foundations of Brentano’s western psychological theories, phenomenological thinking and strong parallels with the characteristic biomedical processes that define the psychology of an individual. Another feature which helps this model to carve it’s own niche is it’s close resemblance to a formal system. Much like a formal model is defined by a set of states and their inter state transitions, this model is also defined by it’s moments and transitional rules between these moments. It is adept in establishing a definite schema which can be ubiquitously populated by any given data reserve and helps in visualising the persona depicted. Thus, we first categorize the temporal (time to time) social media data into pre-defined mental states (concieved via Abhidhamma). These mental states are then popu- lated into the respective temporal moments of our automaton. Finally, they are used to draw various inferences about the lexical psychology and/or personalities of the respective social media users. We bring closure to our work by illustrating the functionality of our model in three different use cases. We first use our model to formalise the abstract idea of an individual in the concept of social machines. We also exemplify how our model tackles sparsity and non-uniformity of data in social media. The second use case is to model the psychological phenomenon of Anxiety, its conception, continuation etc. by means of lexical data available online. We finally introduce the lexico-psychological mental engagement factor to capture the maximum engagement of each individual with any given mental state. As opposed to the psychometric methods used to capture such a factor, our model uses lexical features to compute it. We discuss the importance of each

Abstract

Age old classical problems of the theory of society have a contemporary new dimension with the advent of computer-mediated communication. New communications rapidly transformed the internet introducing the idea of the social web. The concept of social machines as socio-technical systems, in whose machinery both humans and technology play participatory roles has given a new definition of what is meant by the term ‘social’. In this contemporary context of social machines, we intend to develop the concept of a social automaton based on a formal approach to a theory of society. The research presented is a result of analytic inquiry into the origin of society and not historical or empirical inquiry. We ask an ontological question - what is the stuff of which society is made of? After all society is objectified as an entity, to which individuals belong and which somehow is constructed/- constituted by individuals. As a result of our inquiry, we present a symbolic automaton of an individual based on Buddhist Abhidhamma formal ontology of the individual. Further, by focusing on individual’s action we develop a social automaton which is capable of handling social objects like persona, roles, relations, manners, rituals etc. Such an automaton would contribute to evolving a wholesome idea of social machine which have human as well as technology components. With a bottom up approach, we first start by understanding the fabric of an individual human being who is the basic component of a society. We establish that for understanding social reality it is imperative to begin with the formal framework of individual which accommodates this reality. To understand the framework of individual, we take inspiration from Buddhist Abhidhamma theory which provides a formal model of man as a stream of consciousness. With this groundwork, we develop a computational model of an individual as a stochastic automaton. Based on our individual automaton model, we establish that psychological processes within an individual create scope for generation of social objects. As a computational use case, we populate the automaton model with social media data of the individual and provide a machine learning module to identify mental states from social media data. We then introduce the idea of dynamic evolving persona to personality mining, by analyzing the user’s personality in terms of evolving mental factors. Once we lay the foundation for an individual as psychological being, we then proceed to give an in-depth analysis of action in an individual. With action as the key synthetic signature of the individual vi vii as a social being, we extend our individual automaton to a social automaton. Based on action theory and the sequential psychological processes given by Abhidhamma, we introduce action into our individual automaton as a three tuple - intention, body action and consequence. Further, we provide causal closure of one’s action within the framework of individual automaton. This step leads to a social automaton which has within its framework processes to identify and generate social objects such as relations, roles and institutions. With this move, social reality is conceived around the framework of social automaton.

Abstract

In recent years there has been an increased use of wheeled mobile robots for outdoor and unstructured terrain navigation. While traversing uneven terrain wheeled mobile robots are subject to various kinds wheel slip which can lead to localization errors and decrease motor efficiency. The objective of this work is to develop robust modelling techniques for simulating the kinematics, quasi-statics , and dynamics of wheeled mobile robots subject to kinematic and dynamic no-slip constraints and also develop state-of- the-art suspension mechanisms for hybrid wheeled-legged robots in order to obtain reduced slip while traversing on fully 3D uneven terrain. In order to achieve slip free motion on uneven terrain the wheeled / wheeled-legged robots have to be equipped with essential degrees of freedom that would enhance its ability to negotiate undulations on the terrain. First we perform the analysis of a wheeled mobile robot with a passive variable camber (PVC). We develop a methodology for simulating the kinematics of this robot using the techniques of kinematics for dexterous manipulation of multi-fingured palm. We also develop a framework for simulating the quasi-static motion of the robot on uneven terrain .The framework at each instant estimates the contact forces and velocity of the vehicle platform for a given set of joint velocities of the robot. This ensures that the vehicle satisfies not only kinematic no slip constraints but as well as no slip constraints that arise due to relations between traction and contact forces. In general a complete simulation of a WMR on a fully 3D terrain has been a difficult problem to solve. The best efforts so far have provided a simulation that incorporates the wheel ground contact constraints into a set of differential algebraic equations (DAEs) to estimate the full 6dof pose of the vehicle. This work integrates the quasi static contraints within the DAE framework to provide a complete 6dof evolution of vehicle on 3D terrain that respects both kinematic and quasi static constraints. We then present a motion planning algorithm connecting a starting and ending goal positions of a wheeled mobile robot (WMR) with a passive variable camber on a fully 3D uneven terrain without slipping. The overall planning framework is along the lines of the RRT (Rapidly Exploring Random Tree). The curve connecting the adjacent nodes of the RRT is a quasi-static path which is generated using the forward motion problem based on the Peshkin’s minimum energy principle which combines the force and kinematic relationships of the WMR into a nonlinear optimization problem. The output of this optimization routine is a set of ordinary differential equations (ODEs) representing the non-holonomic constraints and wheel ground contact conditions of the robot along with a set of differential algebraic equations (DAEs) representing the geometric/holonomic constraints of the robot. We extend this kinematic framework to develop the complete dynamic state space realization of the WMR on uneven terrain. The framework performs the affine form state-space realization for the WMR on uneven terrain. Second we present two models of Linear Force Actuator based hybrid wheeled-legged robots and propose a strategy to control the wheel ground contact forces to improve the no-slip margin while traversing a fully 3D uneven terrain. The mechanisms we propose aims at controlling the contact forces at the wheels in a way to improve traction as well as to ensure that the vehicle retains a desired posture. Torus wheels are used to simulate a single point of contact with the terrain which enables us to have lateral tilt on lateral surfaces which is essential while operating on fully 3D terrain. We present the quasi-static analysis of each of the mechanisms to depict the ability of the systems to control the wheel ground contact forces while negotiating uneven terrain. The first model of the Linear Force actuator based Vehicle has a one degree of freedom (1 dof ) leg and the second vehicle has a 2 dof leg. We depict the force controllability of these vehicles using feasibility plots and also perform simulations on terrains having varying geometrical parameters as well as on terrains having discontinuities twice as much as the wheel radius.

Abstract

Autonomous navigation of generic monocular quadcopter in the natural and urban environment requires sophisticated mechanism for perception, planning and control. This thesis is primarily focused on describing a framework which performs perception using monocular camera and generates minimum time collision free trajectory and control for any commercial quadcopter flying through cluttered unknown environment. The proposed framework first utilizes supervised learning approach to estimate the dense depth map for video stream obtained from frontal monocular camera. This depth map is initially transformed into Ego Dynamic Space and subsequently, is used for computing locally traversable way-points utilizing binary integer programming methodology. Finally, trajectory planning and control module employs a convex programming technique to generate collision-free trajectory which follows these way-points and produces appropriate control inputs for the quadcopter. These control inputs are computed from the generated trajectory in each update. Hence, they are applicable to achieve closed loop control similar to model predictive controller. A majority of the work in this thesis is attributed to developing a Autonomous Navigation Framework for generic quad copter which incorporates various theoretical concepts into practical implementation with ROS (Robot Operating System) as middle-ware which is running on a conventional desktop computer with Ubuntu 12.04 LTS. The functionality of the proposed approach could be described as a threefold navigation framework: perception, planning and control. The thesis is concluded by demonstrating the applicability of developed navigation framework in controlled indoors and in unstructured natural outdoors environment on a low cost commercial quadcopter ParrotTM Ardrone 2.0.

Abstract

Syntactic parsing, a major component of natural language processing, involves understanding the structure of a sentence as per given rules. Despite of enormous research in this field, syntactic parsing for Indian languages is still not at par with other languages like English. The reason being, most of the Indian languages are morphologically rich, free word-order languages (Mor- FWO) and parsing Mor-FWO languages is a challenging task. Apart from this, parsing requires sizable annotated resource called a treebank for machine learning algorithms. Treebank is a text corpus with manually annotated grammatical analysis of each sentence. Unavailability of resources for these languages is a major reason why we do not have good parsers for these languages. In this work, we have proposed methods which aid in semi-automatic development of good quality treebanks for Indian languages by proposing a method to automatically detect potential errors in treebank. With the help of this approach, manual validators can validate treebanks in less time and effort. Since the availability of Penn Treebank [44], treebanks have played a crucial role in building automatic natural language processing tools for various languages. In particular, treebanks have helped in building robust and efficient syntactic parsers. The availability of syntactic parsers is critical for the further processing of a sentence, e.g. semantic analysis [31]. We, therefore, explore methods to develop treebanks for resource poor languages because we might have huge unannotated data available with us but labeled data is very less for many languages. Anno- tation being an expensive task, if we obtain a tool which helps in building quality treebanks, development of statistical parsers would be affected a lot. Statistical parsers are generally trained on data from a singe domain and evaluation is also generally done on the same domain. While when put into natural language applications, we expect input data from any random domain. In such cases, parsers behave clumsily and their performance degrades drastically from what was evaluated. This might be because, while training parsers on data from one domain, parser might learn domain specific features which might not be valid for other domains. So, while working on these two aspects of grammatical analysis of sentences in Indian languages, I also ventured into exploring whether existing parsers can be made robust for new domains.

Abstract

In this thesis we present the development of a low cost point of care testing(POCT) device for mon- itoring cerebral oxygenation using near infrared spectroscopy (NIRS). The changes in optical measurements of tissue oxy-(HbO2) and deoxy-(Hb) hemoglobin concentration can be monitored using NIRS and these changes can be coupled with anodal transcranial direct current stimulation (tDCS) to find impairments in the cerebral micro-vessels functionality, where impairments in cerebral microvessels functionality may lead to impairments in the cerebrovascular reactivity (CVR). In fact severely reduced CVR predicts the chances of transient ischemic attack and ipsilateral stroke. The feasibility studies on healthy subjects and stroke survivors showed detectable changes in the NIRS responses to anodal tDCS of current density 0.526 A/m 2 . A clinical studyhas been conducted on 14 stroke survivors using our NIRS data acquisition (DAQ) device and our anodal tDCS device. This study revealed that the lesioned hemisphere with impaired circulation showed significantly (p <0.01) less change in HbO2 concentration than the non-lesioned side in response to anodal tDCS. The NIRS sensors used in the study are not disposable as opposed to the present state of the art technology. Our low cost NIRS-DAQ device has been further integrated with the commercially used CE-approved SomaSensor (Covidien, USA). This was done under a ”clinical study in human subjects of a CE-marked device (being used within indication) to evaluate additional parameters.” Here, Medical Devices Directive covers a diverse range of devices that are classified into one of four risk-based Classifications. Our SomaSensor based low-cost NIRS data acquisition device to monitor hemodynamics during non-invasive brain stimulation (NIBS) should be classified as Class IIa - Medium risk, often short-termnon-invasive device. The reusability of the SomaSensor was also tested as the commercially used SomaSensors are labelled as disposable. A large chunk of the complete cost of the NIRS setup comes from the procurement of the CE-approved SomaSensors. By proving the reusability of the SomaSensors the complete cost of the NIRS setup was also reduced. The performance of the SomaSensor interfaced with our low cost NIRS-DAQ device was also tested with the help of a dual layer dynamic phantom filled human blood and intralipid. NIRS and anodal tDCS have been further combined with electroencephalography (EEG) to develop a NIRS-EEG/tDCS joint-imaging method. This was done to investigate local post-tDCS alterations from baseline at the site of anodal tDCS using NIRS-EEG/tDCS joint-imaging. In a separate study the changes in local post-tDCS alterations in motor evoked potentials (MEP)-measure of corticospinal excitability were investigated. We found that post-tDCS changes in the mean regional cerebral oxygen saturation (rSO 2 ) from baseline mostly correlated with the corresponding post-tDCS change in log-transformed mean-power of EEG within 0.5Hz - 11.25Hz frequency band. Moreover, a decrease in log-transformed mean power of EEG within 0.5Hz - 11.25Hz frequency band corresponded with an increase in the MEP-measure of corticospinal excitability found in the second study. Therefore, we propose to combine NIRS-EEG/tDCS joint-imaging with corticospinal excitability investigation in a single study to confirm these findings.