¶ Research Releases: Expanding the Frontier of Nexstem Technology
As Nexstem continues to innovate within the field of neurotechnology, the company consistently enhances its EEG analysis capabilities through the development of advanced operators, algorithms, and future integrations. This article highlights the current suite of tools available in Nexstem technology and outlines future implementations that are set to revolutionize brain-computer interface (BCI) applications and machine learning (ML) integrations in research and clinical practices.
¶ Current Operators and Algorithms
Nexstem’s current arsenal of operators and algorithms includes a wide range of tools designed for robust and intricate EEG data analysis:
- Statistical Operators: Tools like mean, median, skewness, and more for basic statistical analysis.
- Transforms and Filters: Including DFT, PSD, Welch PSD, and Bandpower for frequency analysis.
- Advanced Algorithms: Techniques such as EMD, FastICA, and WT for complex signal processing.
- Filter Operators: Including IIR, FIR, AWT, and ASR for precise noise reduction and signal clarity.
These tools are crucial for researchers and clinicians who require precise and reliable EEG signal processing capabilities. Importantly, all currently implemented operators are accessible via the InstinctSDK and APIs, facilitating easy integration into custom applications and research projects.
You can check the detailed current implemented operators here.
¶ Future Implementations in ML and BCI Algorithms
Nexstem is poised to integrate a series of sophisticated BCI algorithms that promise to enhance the interaction between human cognitive states and external devices. These include:
- Motor Imagery: To identify specific thought patterns related to imagined movements.
- P300 and MRP (Movement Related Potential): Useful in spellers and other applications that rely on user's response times to stimuli.
- C-VEP (Code-Modulated Visual Evoked Potential) and ErrP (Error-Related Potentials): For improving accuracy in user intention detection.
- SSVEP (Steady-State Visual Evoked Potential) and MRCP (Movement-Related Cortical Potentials): For seamless integration of visual stimuli and movement intentions.
- Motion Onset and SCP (Slow Cortical Potentials): For deeper insights into the onset of motion-related brain activities.
- Workload and Eye State Analysis: To assess cognitive load and eye state for enhanced user interface adaptability.
- Oscillatory (Band Powers): For detailed analysis of specific frequency bands related to various brain functions.
¶ Additional Modules and Integrations
- Graph Theory/Network Science: To explore the connectivity and network dynamics within the brain.
- Phase Amplitude Coupling: To understand the relationship between different brain wave frequencies.
- Non-Linear Analyses and Coherence: For advanced signal processing beyond linear assumptions.
- Signal to Noise Ratio and Markers: For improving the quality and accuracy of EEG data interpretation.
- Statistical Tests: Including t-test, F-test, Z scoring, PDF, etc., for rigorous data validation.
Future releases will also focus on integrating multimodal data sources such as:
- Eye Trackers, VR (Virtual Reality), and Monitors: To synchronize visual stimuli with EEG data for comprehensive environment studies.
- Trigger Box and Wrist Band: For enhanced external device control and physiological data integration, providing a holistic view of the user's state and interactions.