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'''[https://www.medusabci.com/ MEDUSA©] is a Python-based open-source software ecosystem to facilitate the creation of brain-computer interface (BCI) systems and neuroscience experiments''' <ref>Santamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., Rodríguez-González, V., Pérez-Velasco, S., Moreno-Calderón, S., & Hornero, R. (2023). MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research. ''Computer methods and programs in biomedicine'', ''230'', 107357, DOI: https://doi.org/10.1016/j.cmpb.2023.107357</ref>. The software boasts a range of features, including complete compatibility with lab streaming layer, a collection of ready-made examples for common BCI paradigms, extensive tutorials and [https://docs.medusabci.com/ documentation], an accessible online app marketplace, and a robust modular design, among others.

'''[https://www.medusabci.com/ MEDUSA©] is a Python-based open-source software ecosystem to facilitate the creation of brain-computer interface (BCI) systems and neuroscience experiments''' <ref>Santamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., Rodríguez-González, V., Pérez-Velasco, S., Moreno-Calderón, S., & Hornero, R. (2023). MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research. ''Computer methods and programs in biomedicine'', ''230'', 107357, DOI: https://doi.org/10.1016/j.cmpb.2023.107357</ref>. The software boasts a range of features, including complete compatibility with lab streaming layer, a collection of ready-made examples for common BCI paradigms, extensive tutorials and [https://docs.medusabci.com/ documentation], an accessible online app marketplace, and a robust modular design, among others. This software has been developed by members of the [[Biomedical Engineering Group]] at the University of Valladolid, Spain.

== Software architecture design ==

== Software architecture design ==

MEDUSA© already supports most state-of-the-art noninvasive BCI paradigms utilized in scientific literature, covering both exogenous and endogenous control signals extracted from electroencephalography (EEG):

MEDUSA© already supports most state-of-the-art noninvasive BCI paradigms utilized in scientific literature, covering both exogenous and endogenous control signals extracted from electroencephalography (EEG):

* '''Code-modulated visual evoked potentials (c-VEP)''': MEDUSA© stands as the only general-purpose system for developing BCIs that supports c-VEP paradigms. This exogenous control signal originates from the primary visual cortex (occipital cortex) when users focus on flashing commands following a pseudorandom sequence <ref name=":0">Martínez-Cagigal, V., Thielen, J., Santamaria-Vazquez, E., Pérez-Velasco, S., Desain, P., & Hornero, R. (2021). Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review. ''Journal of Neural Engineering'', ''18''(6), 061002, DOI: https://doi.org/10.1088/1741-2552/ac38cf</ref>. Different selection commands are encoded with distinct sequences, displaying minimal correlation between them. Typically, this is achieved by employing a time series with a flat autocorrelation profile (e.g., [[wikipedia:Maximum_length_sequence|m-sequences]]) and encoding commands using temporally shifted versions of the original sequence. This approach, known as the circular shifting paradigm, requires a calibration to extract the brain's response to the original code (main template). Templates for the rest of commands are computed by temporally shifting the main template according to each command's lag. This enables online decoding by computing the correlation between the online response and the commands' templates <ref name=":0" />. It has been demonstrated that this approach is able to reach high performances (over 90%, 2-5 s per selection) with small calibration times (1-2 min) <ref name=":0" />. MEDUSA© incorporates several built-in apps utilizing this paradigm: the [https://www.medusabci.com/market/cvep_speller/ "''c-VEP Speller'',"] a speller that utilizes binary m-sequences (black & white flashes) for command encoding; or the [https://www.medusabci.com/market/pary_cvep/ "P-ary c-VEP Speller,"] which employs p-ary m-sequences encoded with different shades of grey (or custom colors) to alleviate visual fatigue <ref>Martínez-Cagigal, V., Santamaría-Vázquez, E., Pérez-Velasco, S., Marcos-Martínez, D., Moreno-Calderón, S., & Hornero, R. (2023). Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs. ''Expert Systems with Applications'', ''232'', 120815, DOI: https://doi.org/10.1016/j.eswa.2023.120815</ref>.

* [[File:C-VEP Keyboard.png|thumb|A c-VEP keyboard speller implemented for MEDUSA Platform.]]'''Code-modulated visual evoked potentials (c-VEP)''': MEDUSA© stands as the only general-purpose system for developing BCIs that supports c-VEP paradigms. This exogenous control signal originates from the primary visual cortex (occipital cortex) when users focus on flashing commands following a pseudorandom sequence <ref name=":0">Martínez-Cagigal, V., Thielen, J., Santamaria-Vazquez, E., Pérez-Velasco, S., Desain, P., & Hornero, R. (2021). Brain–computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review. ''Journal of Neural Engineering'', ''18''(6), 061002, DOI: https://doi.org/10.1088/1741-2552/ac38cf</ref>. Different selection commands are encoded with distinct sequences, displaying minimal correlation between them. Typically, this is achieved by employing a time series with a flat autocorrelation profile (e.g., [[wikipedia:Maximum_length_sequence|m-sequences]]) and encoding commands using temporally shifted versions of the original sequence. This approach, known as the circular shifting paradigm, requires a calibration to extract the brain's response to the original code (main template). Templates for the rest of commands are computed by temporally shifting the main template according to each command's lag. This enables online decoding by computing the correlation between the online response and the commands' templates <ref name=":0" />. It has been demonstrated that this approach is able to reach high performances (over 90%, 2-5 s per selection) with small calibration times (1-2 min) <ref name=":0" />. MEDUSA© incorporates several built-in apps utilizing this paradigm: the [https://www.medusabci.com/market/cvep_speller/ "''c-VEP Speller'',"] a speller that utilizes binary m-sequences (black & white flashes) for command encoding; or the [https://www.medusabci.com/market/pary_cvep/ "P-ary c-VEP Speller,"] which employs p-ary m-sequences encoded with different shades of grey (or custom colors) to alleviate visual fatigue <ref>Martínez-Cagigal, V., Santamaría-Vázquez, E., Pérez-Velasco, S., Marcos-Martínez, D., Moreno-Calderón, S., & Hornero, R. (2023). Non-binary m-sequences for more comfortable brain–computer interfaces based on c-VEPs. ''Expert Systems with Applications'', ''232'', 120815, DOI: https://doi.org/10.1016/j.eswa.2023.120815</ref>.

* '''P300 evoked potentials:''' P300 are positive event-related potentials (ERP) that occur over centro-parietal locations approximately 300 ms after the presentation of an unexpected stimulus that requires attention or cognitive processing . P300s are commonly elicited using [[wikipedia:Oddball_paradigm|oddball]] paradigms, where sequences of repetitive stimuli are infrequently interrupted by a deviant stimulus <ref name=":1">Wolpaw, Jonathan, and Elizabeth Winter Wolpaw (eds), ''Brain–Computer Interfaces: Principles and Practice'' (2012; online edn, Oxford Academic, 24 May 2012), DOI: <nowiki>https://doi.org/10.1093/acprof:oso/9780195388855.001.0001</nowiki></ref>. This can be extended to provide real-time communication using noninvasive BCIs by employing the row-column paradigm (RCP). In this paradigm, a command matrix is presented to the users, with rows and columns randomly flashing. The user must attend to the desired command, generating a P300 component only when the row and column containing that command flash. By detecting the most likely row and column based on the decoding of these P300 components, the system can determine the target command in real-time <ref name=":1" />. MEDUSA© already implements several built-in apps for P300-based BCIs, such as the "[https://www.medusabci.com/market/rcp_speller/ RCP speller]".

* '''P300 evoked potentials:''' P300 are positive event-related potentials (ERP) that occur over centro-parietal locations approximately 300 ms after the presentation of an unexpected stimulus that requires attention or cognitive processing . P300s are commonly elicited using [[wikipedia:Oddball_paradigm|oddball]] paradigms, where sequences of repetitive stimuli are infrequently interrupted by a deviant stimulus <ref name=":1">Wolpaw, Jonathan, and Elizabeth Winter Wolpaw (eds), ''Brain–Computer Interfaces: Principles and Practice'' (2012; online edn, Oxford Academic, 24 May 2012), DOI: <nowiki>https://doi.org/10.1093/acprof:oso/9780195388855.001.0001</nowiki></ref>. This can be extended to provide real-time communication using noninvasive BCIs by employing the row-column paradigm (RCP). In this paradigm, a command matrix is presented to the users, with rows and columns randomly flashing. The user must attend to the desired command, generating a P300 component only when the row and column containing that command flash. By detecting the most likely row and column based on the decoding of these P300 components, the system can determine the target command in real-time <ref name=":1" />. MEDUSA© already implements several built-in apps for P300-based BCIs, such as the "[https://www.medusabci.com/market/rcp_speller/ RCP speller]".

* '''Sensorimotor rhythms (SMR):''' (in progress)

* '''Sensorimotor rhythms (SMR):''' SMRs are rhythmic oscillations of the brain activity over sensorimotor cortices. This rhythmic activity is mainly composed by μ (8-12 Hz), β (18-30 Hz) and γ (> 30 Hz) frequency bands <ref name=":1" />. SMR are characterized by a desynchronization of rhythms during motor behaviours, such as voluntary executed or imagined movements. This decrease is known as event-related desynchronization (ERD) and appears over the contralateral region providing a bilateral symmetry <ref name=":1" />. This characteristic pattern can be used to develop BCI applications in which user can control two-degrees-of-freedom systems by performing motor imagery (typically of upper limbs) <ref name=":1" />. SMR modulation training has also been proposed to restore motor function in post-stroke patients <ref>Sebastián-Romagosa, M., Cho, W., Ortner, R., Murovec, N., Von Oertzen, T., Kamada, K., ... & Guger, C. (2020). Brain computer interface treatment for motor rehabilitation of upper extremity of stroke patients—a feasibility study. ''Frontiers in Neuroscience'', ''14'', 591435.</ref>. MEDUSA© provides a [https://medusabci.com/market/mi/ built-in app for SMR-based BCIs], which one includes classical machine learning algorithm for decoding user intention, common spatial Patterns (CSP) <ref name=":1" />, as well as the state-of-the-art deep learning model EEGSym <ref>Pérez-Velasco, S., Santamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., & Hornero, R. (2022). EEGSym: Overcoming inter-subject variability in motor imagery based BCIs with deep learning. ''IEEE Transactions on Neural Systems and Rehabilitation Engineering'', ''30'', 1766-1775.</ref>.

* '''Neurofeedback (NF):''' (in progress)

* '''Neurofeedback (NF):''' NF is a technique that aims to self-regulate certain patterns of one's own brain activity <ref name=":2">Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., ... & Sulzer, J. (2017). Closed-loop brain training: the science of neurofeedback. ''Nature Reviews Neuroscience'', ''18''(2), 86-100.</ref>. This can be achieved by providing real-time feedback related to these patterns. Through the provided feedback and operant conditioning, the user can find cognitive strategies to reach a mental state related to the desired brain activity pattern. This learning process is believed to induce brain plasticity, reinforcing the neural networks involved in the trained brain activity pattern and leading to an improvement in the cognitive functions associated with these neural networks <ref name=":2" />.  NFs have been proposed for several clinical applications, such as the treatment of major depressive disorder, attention deficit hyperactivity disorder as well as for the improvement of cognitive functions in healthy individuals <ref>Ros, T., J. Baars, B., Lanius, R. A., & Vuilleumier, P. (2014). Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. ''Frontiers in human neuroscience'', ''8'', 1008.</ref>. MEDUSA© implements [https://medusabci.com/market/itaca/ ITACA Neurofeedback training], an app designed for conducting NF training studies [https://medusabci.com/market/itaca/]. It includes three different training scenarios with a gamified design and training metrics based on both power and connectivity to provide feedback on different brain patterns <ref name=":3">Marcos-Martínez, D., Santamaría-Vázquez, E., Martínez-Cagigal, V., Pérez-Velasco, S., Rodríguez-González, V., Martín-Fernández, A., ... & Hornero, R. (2023). ITACA: An open-source framework for Neurofeedback based on Brain–Computer Interfaces. ''Computers in Biology and Medicine'', ''160'', 107011.</ref>.

== Supported cognitive psychology tests ==

== Supported cognitive psychology tests ==

(in progress)

 

* '''Corsi Block-Tapping Test''': This test assesses visuo-spatial short-term working memory. It involves mimicking a sequence of up to nine blocks. The sequence starts out simple and becomes more complex until the user’s performance suffers <ref name=":3" />. The [https://medusabci.com/market/corsi/ version provided by MEDUSA©] includes both a "forward" (repeating the sequence in the same order in which it was presented) and a "backward" (repeating the sequence in reverse order) modes.

* '''Oddball Paradigm''': The oddball paradigm is a technique used in evoked potential research in which visual or auditory stimuli are used to assess the neural reactions to unpredictable but recognizable events <ref>García-Larrea, L., Lukaszewicz, A. C., & Mauguiére, F. (1992). Revisiting the oddball paradigm. Non-target vs neutral stimuli and the evaluation of ERP attentional effects. ''Neuropsychologia'', ''30''(8), 723-741.</ref>. The subject is asked to react either by counting or by button pressing incidences of target stimuli that are hidden as rare occurrences amongst a series of more common stimuli, that often require no response. It has been found that an evoked research potential across the parieto-central area of the skull that is usually around 300 ms and called P300 is larger after the target stimulus. Two apps are available at MEDUSA©: a [https://medusabci.com/market/oddball/ visual & auditory oddball paradigm], an a [https://medusabci.com/market/rt_vot/ visual oddball task] with real-time monitoring of the ERP for demonstration purposes.

==Links==

==Links==