# Literature Map

> Generated from repository source files. Do not edit this page directly; edit the source file listed below and rerun `vp run docs:generate`.

> Source: `literature/00-literature-map.md`

## Literature Map

This map expands the four-track structure in `README.md` into seven review lanes. The expansion separates foundations, robot grasping, and shared autonomy so the final paper is not built from only SSVEP/MI/YOLO method papers.

Current corpus boundary: this map tracks the active 73-paper local corpus with 73 extracted local text artifacts and 73 structured paper cards.

## Track A: BCI / EEG Foundations

Primary questions:

* What are the established limits and evaluation norms for EEG-based BCI?
* Which EEG classifier families are relevant to both SSVEP and MI?
* What system and usability lessons matter for an online BCI-robot experiment?

Current sources: `BCI-001` to `BCI-005`.

## Track B: SSVEP

Primary questions:

* What mechanisms and paradigms support SSVEP as a discrete target-selection channel?
* Which algorithms support short-window, low-channel SSVEP decoding?
* What evidence exists for dynamic, scene-based, AR, or object-level SSVEP targets?

Current sources: `SSVEP-001`, `SSVEP-003`, `SSVEP-004`, `SSVEP-005`, `SSVEP-007`, `SSVEP-010`, `SSVEP-011`, `SSVEP-012`, `SSVEP-013`, `SSVEP-014`, `SSVEP-016`, `SSVEP-019`, `SSVEP-020`, `SSVEP-021`, `SSVEP-022`, `SSVEP-023`, `SSVEP-026`, `SSVEP-027`, `SSVEP-028`, `SSVEP-029`, `SSVEP-030`, `SSVEP-031`.

## Track C: Motor Imagery

Primary questions:

* What ERD/ERS and channel evidence supports MI mode-control design?
* Which decoding methods are practical for low-channel, online control?
* What evidence supports MI as intervention/confirmation rather than low-level robot control?

Current sources: `MI-002`, `MI-004`, `MI-005`, `MI-006`, `MI-007`, `MI-008`, `MI-009`, `MI-013`, `MI-014`, `MI-016`, `MI-017`, `MI-018`, `MI-020`, `MI-021`, `MI-022`, `MI-023`, `MI-024`, `MI-025`, `MI-027`, `MI-028`, `MI-029`, `MI-030`, `MI-031`, `MI-032`.

## Track D: YOLO / Detection

Primary questions:

* How should object detection outputs become BCI-selectable candidates?
* What are the failure modes from detection jitter, missed detections, and class confusion?
* Which detector paradigms are appropriate baselines for YOLO-style real-time candidate generation?

Current sources: `YOLO-001`, `YOLO-002`, `YOLO-003`, `YOLO-004`, `YOLO-005`, `YOLO-007`, `YOLO-009`, `YOLO-010`.

## Track E: Robot Vision / Grasping / Calibration

Primary questions:

* Where is the boundary between object detection and grasp-pose estimation?
* Which grasping methods can turn selected objects into executable grasp poses?
* What calibration evidence is needed for camera-to-robot coordinate transforms?

Current sources: `GRASP-001`, `GRASP-003`, `GRASP-004`, `GRASP-005`, `GRASP-006`, `GRASP-007`.

## Track F: Hybrid BCI / Brain-Robot Interface

Primary questions:

* How do hybrid SSVEP-MI systems fuse intent and confirmation?
* What shared-control evidence supports shifting low-level execution to robot autonomy?
* Is dynamic command space a novel contribution relative to existing vision-guided BCI systems?

Current sources: `BRI-001` to `BRI-006`.

## Track G: Shared Autonomy / Shared Control

Primary questions:

* What formal shared-control methods justify splitting high-level intent and low-level execution?
* Which baselines should be used for fixed command space vs scene-aware command space?
* How should recovery, hindsight optimization, policy blending, and task abstraction inform the state machine?

Current sources: `SA-001`, `SA-002`.